Overview

Brought to you by YData

Dataset statistics

Number of variables55
Number of observations999
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory4.4 KiB

Variable types

Text24
Numeric1
Categorical30

Alerts

Authority_Present is highly overall correlated with Clarity_and_Conciseness_Value and 29 other fieldsHigh correlation
Clarity_and_Conciseness_Value is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Contains_Colon is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Contains_Exclamation_Mark is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Contains_Hyphen is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Contains_Numbers is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Contains_Question_Mark is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Contains_Quotes is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Curiosity_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Economic_Benefit_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Ends_With_Question_Mark is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Exclusivity_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Exclusivity_Words is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Fear_Concern_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Hope_Optimism_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Indignation_Controversy_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Length_General_Assessment is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Main_Category is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Main_Classification is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
National_Relevance_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Originality_and_Differentiation_Value is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Personal_Identification_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Prohibition_Restriction_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Recognized_Brand_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Relevance_and_Timeliness_Value is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Solution_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Starts_With_Number is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Strategic_Keyword_Usage_Value is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Surprise_Awe_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Temporal_Urgency_Present is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Visibility is highly overall correlated with Authority_Present and 29 other fieldsHigh correlation
Clarity_and_Conciseness_Value is highly imbalanced (74.4%) Imbalance
Relevance_and_Timeliness_Value is highly imbalanced (63.0%) Imbalance
Strategic_Keyword_Usage_Value is highly imbalanced (65.7%) Imbalance
Contains_Question_Mark is highly imbalanced (63.8%) Imbalance
Contains_Exclamation_Mark is highly imbalanced (75.3%) Imbalance
Starts_With_Number is highly imbalanced (51.9%) Imbalance
Ends_With_Question_Mark is highly imbalanced (67.6%) Imbalance
Length_General_Assessment is highly imbalanced (58.4%) Imbalance
Exclusivity_Present is highly imbalanced (64.1%) Imbalance
Exclusivity_Words is highly imbalanced (85.0%) Imbalance
Prohibition_Restriction_Present is highly imbalanced (55.7%) Imbalance
Title has unique values Unique
Visibility has unique values Unique

Reproduction

Analysis started2025-07-03 06:20:42.056428
Analysis finished2025-07-03 06:20:58.909575
Duration16.85 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Title
Text

Unique 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size180.1 KiB
2025-07-03T06:20:59.285565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length263
Median length145
Mean length83.154154
Min length20

Characters and Unicode

Total characters83071
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique999 ?
Unique (%)100.0%

Sample

1st rowFord is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand
2nd rowNew U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above
3rd rowCadets who met all Air Force Academy graduation standards denied commissions because they’re transgender
4th rowThe DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently
5th rowMcDonald's Removing 1 Breakfast Menu Item for Good on July 2
ValueCountFrequency (%)
to 418
 
3.0%
the 372
 
2.7%
in 274
 
2.0%
of 210
 
1.5%
a 197
 
1.4%
and 195
 
1.4%
for 194
 
1.4%
new 143
 
1.0%
with 104
 
0.8%
is 98
 
0.7%
Other values (4244) 11532
83.9%
2025-07-03T06:20:59.845985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12738
15.3%
e 7675
 
9.2%
a 4983
 
6.0%
o 4803
 
5.8%
t 4685
 
5.6%
r 4540
 
5.5%
n 4437
 
5.3%
i 4398
 
5.3%
s 4306
 
5.2%
l 2860
 
3.4%
Other values (82) 27646
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12738
15.3%
e 7675
 
9.2%
a 4983
 
6.0%
o 4803
 
5.8%
t 4685
 
5.6%
r 4540
 
5.5%
n 4437
 
5.3%
i 4398
 
5.3%
s 4306
 
5.2%
l 2860
 
3.4%
Other values (82) 27646
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12738
15.3%
e 7675
 
9.2%
a 4983
 
6.0%
o 4803
 
5.8%
t 4685
 
5.6%
r 4540
 
5.5%
n 4437
 
5.3%
i 4398
 
5.3%
s 4306
 
5.2%
l 2860
 
3.4%
Other values (82) 27646
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12738
15.3%
e 7675
 
9.2%
a 4983
 
6.0%
o 4803
 
5.8%
t 4685
 
5.6%
r 4540
 
5.5%
n 4437
 
5.3%
i 4398
 
5.3%
s 4306
 
5.2%
l 2860
 
3.4%
Other values (82) 27646
33.3%

Visibility
Real number (ℝ)

High correlation  Unique 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2071178.6
Minimum582886
Maximum22572066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-03T06:20:59.980065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum582886
5-th percentile621386.7
Q1799717
median1215990
Q32304652
95-th percentile6645731.1
Maximum22572066
Range21989180
Interquartile range (IQR)1504935

Descriptive statistics

Standard deviation2399035.7
Coefficient of variation (CV)1.1582949
Kurtosis19.576633
Mean2071178.6
Median Absolute Deviation (MAD)501087
Skewness3.7796256
Sum2.0691074 × 109
Variance5.7553721 × 1012
MonotonicityStrictly decreasing
2025-07-03T06:21:00.119856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
582886 1
 
0.1%
22572066 1
 
0.1%
21331409 1
 
0.1%
19344936 1
 
0.1%
18797641 1
 
0.1%
16353543 1
 
0.1%
15318643 1
 
0.1%
596558 1
 
0.1%
596772 1
 
0.1%
597265 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
582886 1
0.1%
583765 1
0.1%
585267 1
0.1%
585417 1
0.1%
585428 1
0.1%
586529 1
0.1%
587557 1
0.1%
588887 1
0.1%
589230 1
0.1%
590578 1
0.1%
ValueCountFrequency (%)
22572066 1
0.1%
21331409 1
0.1%
19344936 1
0.1%
18797641 1
0.1%
16353543 1
0.1%
15318643 1
0.1%
14627061 1
0.1%
13507301 1
0.1%
13339952 1
0.1%
13255807 1
0.1%
Distinct931
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size169.5 KiB
2025-07-03T06:21:00.484557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length263
Median length142
Mean length76.531532
Min length0

Characters and Unicode

Total characters76455
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique930 ?
Unique (%)93.1%

Sample

1st rowFord is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand
2nd rowNew U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above
3rd rowCadets who met all Air Force Academy graduation standards denied commissions because they’re transgender
4th rowThe DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently
5th rowMcDonald's Removing 1 Breakfast Menu Item for Good on July 2
ValueCountFrequency (%)
to 387
 
3.1%
the 352
 
2.8%
in 248
 
2.0%
of 193
 
1.5%
a 186
 
1.5%
for 181
 
1.4%
and 172
 
1.4%
new 134
 
1.1%
with 96
 
0.8%
is 93
 
0.7%
Other values (4031) 10630
83.9%
2025-07-03T06:21:01.034456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11742
15.4%
e 7066
 
9.2%
a 4541
 
5.9%
o 4483
 
5.9%
t 4327
 
5.7%
r 4172
 
5.5%
n 4086
 
5.3%
i 4059
 
5.3%
s 3963
 
5.2%
l 2637
 
3.4%
Other values (81) 25379
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11742
15.4%
e 7066
 
9.2%
a 4541
 
5.9%
o 4483
 
5.9%
t 4327
 
5.7%
r 4172
 
5.5%
n 4086
 
5.3%
i 4059
 
5.3%
s 3963
 
5.2%
l 2637
 
3.4%
Other values (81) 25379
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11742
15.4%
e 7066
 
9.2%
a 4541
 
5.9%
o 4483
 
5.9%
t 4327
 
5.7%
r 4172
 
5.5%
n 4086
 
5.3%
i 4059
 
5.3%
s 3963
 
5.2%
l 2637
 
3.4%
Other values (81) 25379
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11742
15.4%
e 7066
 
9.2%
a 4541
 
5.9%
o 4483
 
5.9%
t 4327
 
5.7%
r 4172
 
5.5%
n 4086
 
5.3%
i 4059
 
5.3%
s 3963
 
5.2%
l 2637
 
3.4%
Other values (81) 25379
33.2%

Main_Category
Categorical

High correlation 

Distinct16
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
News_and_Current_Events
227 
Finance_and_Business
168 
Entertainment_and_Culture
90 
Gastronomy
86 
69 
Other values (11)
359 

Length

Max length26
Median length23
Mean length16.592593
Min length0

Characters and Unicode

Total characters16576
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowFinance_and_Business
2nd rowNews_and_Current_Events
3rd rowNews_and_Current_Events
4th rowNews_and_Current_Events
5th rowGastronomy

Common Values

ValueCountFrequency (%)
News_and_Current_Events 227
22.7%
Finance_and_Business 168
16.8%
Entertainment_and_Culture 90
 
9.0%
Gastronomy 86
 
8.6%
69
 
6.9%
Science 64
 
6.4%
Health_and_Wellness 57
 
5.7%
Home_and_Lifestyle 46
 
4.6%
Travel 45
 
4.5%
Curiosities_and_Miscellany 38
 
3.8%
Other values (6) 109
10.9%

Length

2025-07-03T06:21:01.156382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
news_and_current_events 227
24.4%
finance_and_business 168
18.1%
entertainment_and_culture 90
 
9.7%
gastronomy 86
 
9.2%
science 64
 
6.9%
health_and_wellness 57
 
6.1%
home_and_lifestyle 46
 
4.9%
travel 45
 
4.8%
curiosities_and_miscellany 38
 
4.1%
sports 36
 
3.9%
Other values (5) 73
 
7.8%

Most occurring characters

ValueCountFrequency (%)
n 2158
13.0%
e 1914
11.5%
_ 1561
 
9.4%
s 1381
 
8.3%
a 1197
 
7.2%
t 1137
 
6.9%
r 839
 
5.1%
i 774
 
4.7%
u 696
 
4.2%
d 695
 
4.2%
Other values (25) 4224
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2158
13.0%
e 1914
11.5%
_ 1561
 
9.4%
s 1381
 
8.3%
a 1197
 
7.2%
t 1137
 
6.9%
r 839
 
5.1%
i 774
 
4.7%
u 696
 
4.2%
d 695
 
4.2%
Other values (25) 4224
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2158
13.0%
e 1914
11.5%
_ 1561
 
9.4%
s 1381
 
8.3%
a 1197
 
7.2%
t 1137
 
6.9%
r 839
 
5.1%
i 774
 
4.7%
u 696
 
4.2%
d 695
 
4.2%
Other values (25) 4224
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2158
13.0%
e 1914
11.5%
_ 1561
 
9.4%
s 1381
 
8.3%
a 1197
 
7.2%
t 1137
 
6.9%
r 839
 
5.1%
i 774
 
4.7%
u 696
 
4.2%
d 695
 
4.2%
Other values (25) 4224
25.5%
Distinct113
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
2025-07-03T06:21:01.349826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length22
Mean length15.001001
Min length0

Characters and Unicode

Total characters14986
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)4.6%

Sample

1st rowCompanies & Entrepreneurship
2nd rowPolitics
3rd rowPolitics
4th rowPolitics
5th rowRestaurants & Chefs
ValueCountFrequency (%)
326
 
18.3%
companies 64
 
3.6%
entrepreneurship 64
 
3.6%
politics 53
 
3.0%
crime 53
 
3.0%
judicial 53
 
3.0%
recipes 53
 
3.0%
celebrities 51
 
2.9%
influencers 51
 
2.9%
personal 44
 
2.5%
Other values (143) 972
54.5%
2025-07-03T06:21:01.804677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1592
 
10.6%
i 1400
 
9.3%
n 1327
 
8.9%
s 1025
 
6.8%
r 1005
 
6.7%
t 934
 
6.2%
a 886
 
5.9%
854
 
5.7%
o 700
 
4.7%
l 612
 
4.1%
Other values (41) 4651
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1592
 
10.6%
i 1400
 
9.3%
n 1327
 
8.9%
s 1025
 
6.8%
r 1005
 
6.7%
t 934
 
6.2%
a 886
 
5.9%
854
 
5.7%
o 700
 
4.7%
l 612
 
4.1%
Other values (41) 4651
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1592
 
10.6%
i 1400
 
9.3%
n 1327
 
8.9%
s 1025
 
6.8%
r 1005
 
6.7%
t 934
 
6.2%
a 886
 
5.9%
854
 
5.7%
o 700
 
4.7%
l 612
 
4.1%
Other values (41) 4651
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1592
 
10.6%
i 1400
 
9.3%
n 1327
 
8.9%
s 1025
 
6.8%
r 1005
 
6.7%
t 934
 
6.2%
a 886
 
5.9%
854
 
5.7%
o 700
 
4.7%
l 612
 
4.1%
Other values (41) 4651
31.0%
Distinct95
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size60.6 KiB
2025-07-03T06:21:02.171142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length3
Mean length4.9449449
Min length0

Characters and Unicode

Total characters4940
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)7.1%

Sample

1st rowN/A
2nd rowGovernment
3rd rowGovernment
4th rowGovernment
5th rowN/A
ValueCountFrequency (%)
n/a 631
61.9%
government 40
 
3.9%
tips 30
 
2.9%
advances 21
 
2.1%
main 21
 
2.1%
courses 21
 
2.1%
national 20
 
2.0%
desserts 19
 
1.9%
17
 
1.7%
prevention 14
 
1.4%
Other values (114) 186
 
18.2%
2025-07-03T06:21:02.716851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 675
13.7%
N 655
13.3%
/ 633
12.8%
e 381
 
7.7%
s 310
 
6.3%
n 266
 
5.4%
i 242
 
4.9%
t 186
 
3.8%
r 180
 
3.6%
a 174
 
3.5%
Other values (41) 1238
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 675
13.7%
N 655
13.3%
/ 633
12.8%
e 381
 
7.7%
s 310
 
6.3%
n 266
 
5.4%
i 242
 
4.9%
t 186
 
3.8%
r 180
 
3.6%
a 174
 
3.5%
Other values (41) 1238
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 675
13.7%
N 655
13.3%
/ 633
12.8%
e 381
 
7.7%
s 310
 
6.3%
n 266
 
5.4%
i 242
 
4.9%
t 186
 
3.8%
r 180
 
3.6%
a 174
 
3.5%
Other values (41) 1238
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 675
13.7%
N 655
13.3%
/ 633
12.8%
e 381
 
7.7%
s 310
 
6.3%
n 266
 
5.4%
i 242
 
4.9%
t 186
 
3.8%
r 180
 
3.6%
a 174
 
3.5%
Other values (41) 1238
25.1%

Clarity_and_Conciseness_Value
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.4 KiB
High
910 
 
69
Medium
 
18
Low
 
2

Length

Max length6
Median length4
Mean length3.7577578
Min length0

Characters and Unicode

Total characters3754
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 910
91.1%
69
 
6.9%
Medium 18
 
1.8%
Low 2
 
0.2%

Length

2025-07-03T06:21:02.892086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:03.005864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 910
97.8%
medium 18
 
1.9%
low 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 928
24.7%
H 910
24.2%
g 910
24.2%
h 910
24.2%
M 18
 
0.5%
e 18
 
0.5%
d 18
 
0.5%
u 18
 
0.5%
m 18
 
0.5%
L 2
 
0.1%
Other values (2) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 928
24.7%
H 910
24.2%
g 910
24.2%
h 910
24.2%
M 18
 
0.5%
e 18
 
0.5%
d 18
 
0.5%
u 18
 
0.5%
m 18
 
0.5%
L 2
 
0.1%
Other values (2) 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 928
24.7%
H 910
24.2%
g 910
24.2%
h 910
24.2%
M 18
 
0.5%
e 18
 
0.5%
d 18
 
0.5%
u 18
 
0.5%
m 18
 
0.5%
L 2
 
0.1%
Other values (2) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 928
24.7%
H 910
24.2%
g 910
24.2%
h 910
24.2%
M 18
 
0.5%
e 18
 
0.5%
d 18
 
0.5%
u 18
 
0.5%
m 18
 
0.5%
L 2
 
0.1%
Other values (2) 4
 
0.1%
Distinct815
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size138.8 KiB
2025-07-03T06:21:03.399275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length205
Median length144
Mean length84.118118
Min length0

Characters and Unicode

Total characters84034
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique790 ?
Unique (%)79.1%

Sample

1st rowThe main message is very clear and easy to understand, detailing Ford's production halt and the CEO's admission of struggle.
2nd rowThe main message is exceptionally clear, detailing who, what, and when without ambiguity.
3rd rowThe main message is very clear and direct, stating precisely what occurred and why.
4th rowThe main message is very clear and direct, stating the change, its source, and its impact.
5th rowThe message is clear and to the point, identifying the brand, action, and date.
ValueCountFrequency (%)
the 1587
 
11.7%
and 1114
 
8.2%
is 826
 
6.1%
message 584
 
4.3%
clear 572
 
4.2%
main 554
 
4.1%
to 540
 
4.0%
easy 459
 
3.4%
understand 444
 
3.3%
headline 319
 
2.3%
Other values (1582) 6584
48.5%
2025-07-03T06:21:04.095301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12653
15.1%
e 9351
11.1%
a 7108
 
8.5%
t 5669
 
6.7%
n 5543
 
6.6%
s 5535
 
6.6%
i 5345
 
6.4%
r 3669
 
4.4%
d 3527
 
4.2%
c 2932
 
3.5%
Other values (68) 22702
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12653
15.1%
e 9351
11.1%
a 7108
 
8.5%
t 5669
 
6.7%
n 5543
 
6.6%
s 5535
 
6.6%
i 5345
 
6.4%
r 3669
 
4.4%
d 3527
 
4.2%
c 2932
 
3.5%
Other values (68) 22702
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12653
15.1%
e 9351
11.1%
a 7108
 
8.5%
t 5669
 
6.7%
n 5543
 
6.6%
s 5535
 
6.6%
i 5345
 
6.4%
r 3669
 
4.4%
d 3527
 
4.2%
c 2932
 
3.5%
Other values (68) 22702
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12653
15.1%
e 9351
11.1%
a 7108
 
8.5%
t 5669
 
6.7%
n 5543
 
6.6%
s 5535
 
6.6%
i 5345
 
6.4%
r 3669
 
4.4%
d 3527
 
4.2%
c 2932
 
3.5%
Other values (68) 22702
27.0%

Relevance_and_Timeliness_Value
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.4 KiB
High
893 
 
69
Medium
 
37

Length

Max length6
Median length4
Mean length3.7977978
Min length0

Characters and Unicode

Total characters3794
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 893
89.4%
69
 
6.9%
Medium 37
 
3.7%

Length

2025-07-03T06:21:04.235525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:04.304273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 893
96.0%
medium 37
 
4.0%

Most occurring characters

ValueCountFrequency (%)
i 930
24.5%
H 893
23.5%
g 893
23.5%
h 893
23.5%
M 37
 
1.0%
e 37
 
1.0%
d 37
 
1.0%
u 37
 
1.0%
m 37
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 930
24.5%
H 893
23.5%
g 893
23.5%
h 893
23.5%
M 37
 
1.0%
e 37
 
1.0%
d 37
 
1.0%
u 37
 
1.0%
m 37
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 930
24.5%
H 893
23.5%
g 893
23.5%
h 893
23.5%
M 37
 
1.0%
e 37
 
1.0%
d 37
 
1.0%
u 37
 
1.0%
m 37
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 930
24.5%
H 893
23.5%
g 893
23.5%
h 893
23.5%
M 37
 
1.0%
e 37
 
1.0%
d 37
 
1.0%
u 37
 
1.0%
m 37
 
1.0%
Distinct931
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
2025-07-03T06:21:04.546478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length243
Median length165
Mean length105.42442
Min length0

Characters and Unicode

Total characters105319
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique930 ?
Unique (%)93.1%

Sample

1st rowThe headline discusses a significant event for a major global brand, which is highly relevant and timely for business and general news audiences.
2nd rowHighly relevant to a specific, large demographic (seniors/drivers) and provides timely information about a future change.
3rd rowThe topic of transgender rights and military policy is highly current and relevant, resonating with ongoing social and political discussions.
4th rowHighly relevant as it affects a large demographic ('millions of drivers') and requires urgent action, indicating timeliness.
5th rowHighly relevant for McDonald's customers and tied to a specific future date, July 2.
ValueCountFrequency (%)
and 1123
 
7.2%
a 594
 
3.8%
relevant 550
 
3.5%
of 432
 
2.8%
to 430
 
2.8%
highly 418
 
2.7%
the 418
 
2.7%
are 368
 
2.4%
interest 357
 
2.3%
is 325
 
2.1%
Other values (1927) 10504
67.7%
2025-07-03T06:21:05.008457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14589
13.9%
e 11710
11.1%
n 7705
 
7.3%
i 7639
 
7.3%
a 7356
 
7.0%
t 6855
 
6.5%
r 5802
 
5.5%
s 5466
 
5.2%
o 4939
 
4.7%
l 4614
 
4.4%
Other values (66) 28644
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14589
13.9%
e 11710
11.1%
n 7705
 
7.3%
i 7639
 
7.3%
a 7356
 
7.0%
t 6855
 
6.5%
r 5802
 
5.5%
s 5466
 
5.2%
o 4939
 
4.7%
l 4614
 
4.4%
Other values (66) 28644
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14589
13.9%
e 11710
11.1%
n 7705
 
7.3%
i 7639
 
7.3%
a 7356
 
7.0%
t 6855
 
6.5%
r 5802
 
5.5%
s 5466
 
5.2%
o 4939
 
4.7%
l 4614
 
4.4%
Other values (66) 28644
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14589
13.9%
e 11710
11.1%
n 7705
 
7.3%
i 7639
 
7.3%
a 7356
 
7.0%
t 6855
 
6.5%
r 5802
 
5.5%
s 5466
 
5.2%
o 4939
 
4.7%
l 4614
 
4.4%
Other values (66) 28644
27.2%

Strategic_Keyword_Usage_Value
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.4 KiB
High
902 
 
69
Medium
 
28

Length

Max length6
Median length4
Mean length3.7797798
Min length0

Characters and Unicode

Total characters3776
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 902
90.3%
69
 
6.9%
Medium 28
 
2.8%

Length

2025-07-03T06:21:05.136290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:05.202457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 902
97.0%
medium 28
 
3.0%

Most occurring characters

ValueCountFrequency (%)
i 930
24.6%
H 902
23.9%
g 902
23.9%
h 902
23.9%
M 28
 
0.7%
e 28
 
0.7%
d 28
 
0.7%
u 28
 
0.7%
m 28
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 930
24.6%
H 902
23.9%
g 902
23.9%
h 902
23.9%
M 28
 
0.7%
e 28
 
0.7%
d 28
 
0.7%
u 28
 
0.7%
m 28
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 930
24.6%
H 902
23.9%
g 902
23.9%
h 902
23.9%
M 28
 
0.7%
e 28
 
0.7%
d 28
 
0.7%
u 28
 
0.7%
m 28
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 930
24.6%
H 902
23.9%
g 902
23.9%
h 902
23.9%
M 28
 
0.7%
e 28
 
0.7%
d 28
 
0.7%
u 28
 
0.7%
m 28
 
0.7%
Distinct931
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size179.7 KiB
2025-07-03T06:21:05.497327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length241
Median length179
Mean length119.74575
Min length0

Characters and Unicode

Total characters119626
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique930 ?
Unique (%)93.1%

Sample

1st rowKey terms like 'Ford', 'factories', 'car production', 'CEO', and 'struggle' are used effectively, making the headline highly discoverable and appealing.
2nd rowContains highly relevant keywords like 'U.S. Driving License Rule', 'Seniors', 'July 2025', 'Essential Changes', and 'Drivers Aged 70 and Above'.
3rd rowUses strong, specific keywords like 'Cadets', 'Air Force Academy', 'denied commissions', and 'transgender', which are highly relevant to the subject matter and discoverable.
4th rowUses strong keywords like 'DMV', 'United States', 'expired license', 'drivers', and 'renew urgently', which are highly searchable and relevant.
5th rowUses strong keywords like 'McDonald's', 'Breakfast Menu', and 'Removing', appealing to target audience interests.
ValueCountFrequency (%)
and 1464
 
8.7%
keywords 767
 
4.5%
like 745
 
4.4%
are 699
 
4.1%
relevant 684
 
4.0%
highly 556
 
3.3%
to 476
 
2.8%
uses 443
 
2.6%
the 391
 
2.3%
audience 290
 
1.7%
Other values (3007) 10387
61.5%
2025-07-03T06:21:06.275894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15973
 
13.4%
e 12652
 
10.6%
a 8281
 
6.9%
r 6815
 
5.7%
t 6440
 
5.4%
n 6323
 
5.3%
i 6224
 
5.2%
s 5965
 
5.0%
l 5023
 
4.2%
o 4758
 
4.0%
Other values (71) 41172
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15973
 
13.4%
e 12652
 
10.6%
a 8281
 
6.9%
r 6815
 
5.7%
t 6440
 
5.4%
n 6323
 
5.3%
i 6224
 
5.2%
s 5965
 
5.0%
l 5023
 
4.2%
o 4758
 
4.0%
Other values (71) 41172
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15973
 
13.4%
e 12652
 
10.6%
a 8281
 
6.9%
r 6815
 
5.7%
t 6440
 
5.4%
n 6323
 
5.3%
i 6224
 
5.2%
s 5965
 
5.0%
l 5023
 
4.2%
o 4758
 
4.0%
Other values (71) 41172
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15973
 
13.4%
e 12652
 
10.6%
a 8281
 
6.9%
r 6815
 
5.7%
t 6440
 
5.4%
n 6323
 
5.3%
i 6224
 
5.2%
s 5965
 
5.0%
l 5023
 
4.2%
o 4758
 
4.0%
Other values (71) 41172
34.4%

Originality_and_Differentiation_Value
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
Medium
691 
High
207 
 
69
Low
 
32

Length

Max length6
Median length6
Mean length5.0750751
Min length0

Characters and Unicode

Total characters5070
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 691
69.2%
High 207
 
20.7%
69
 
6.9%
Low 32
 
3.2%

Length

2025-07-03T06:21:06.403391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:06.479731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 691
74.3%
high 207
 
22.3%
low 32
 
3.4%

Most occurring characters

ValueCountFrequency (%)
i 898
17.7%
M 691
13.6%
e 691
13.6%
d 691
13.6%
u 691
13.6%
m 691
13.6%
H 207
 
4.1%
g 207
 
4.1%
h 207
 
4.1%
L 32
 
0.6%
Other values (2) 64
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 898
17.7%
M 691
13.6%
e 691
13.6%
d 691
13.6%
u 691
13.6%
m 691
13.6%
H 207
 
4.1%
g 207
 
4.1%
h 207
 
4.1%
L 32
 
0.6%
Other values (2) 64
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 898
17.7%
M 691
13.6%
e 691
13.6%
d 691
13.6%
u 691
13.6%
m 691
13.6%
H 207
 
4.1%
g 207
 
4.1%
h 207
 
4.1%
L 32
 
0.6%
Other values (2) 64
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 898
17.7%
M 691
13.6%
e 691
13.6%
d 691
13.6%
u 691
13.6%
m 691
13.6%
H 207
 
4.1%
g 207
 
4.1%
h 207
 
4.1%
L 32
 
0.6%
Other values (2) 64
 
1.3%
Distinct931
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size181.0 KiB
2025-07-03T06:21:06.758381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length255
Median length180
Mean length121.74174
Min length0

Characters and Unicode

Total characters121620
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique930 ?
Unique (%)93.1%

Sample

1st rowWhile the subject matter is impactful, the phrasing is a standard news report style, not particularly unique in its linguistic approach.
2nd rowWhile a standard news announcement format, the specific details make it distinct, yet it lacks a unique angle or creative phrasing.
3rd rowThe specific event described is notable, though the broader subject of transgender individuals in the military has been discussed before. It offers a distinct event within a known debate.
4th rowWhile the specific policy change is unique, the headline structure (authority confirms X - consequence) is common. Its strength lies in the immediate, widespread impact.
5th rowWhile common for fast-food news, the specificity of '1 Breakfast Menu Item' and 'for Good' provides some differentiation.
ValueCountFrequency (%)
the 1557
 
8.3%
a 1019
 
5.4%
and 671
 
3.6%
is 665
 
3.5%
specific 523
 
2.8%
of 515
 
2.7%
unique 508
 
2.7%
common 475
 
2.5%
while 464
 
2.5%
it 322
 
1.7%
Other values (2504) 12096
64.3%
2025-07-03T06:21:07.237483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17885
14.7%
e 11900
 
9.8%
i 9674
 
8.0%
n 7922
 
6.5%
t 7902
 
6.5%
a 7680
 
6.3%
o 6261
 
5.1%
s 5907
 
4.9%
r 5213
 
4.3%
c 4236
 
3.5%
Other values (78) 37040
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17885
14.7%
e 11900
 
9.8%
i 9674
 
8.0%
n 7922
 
6.5%
t 7902
 
6.5%
a 7680
 
6.3%
o 6261
 
5.1%
s 5907
 
4.9%
r 5213
 
4.3%
c 4236
 
3.5%
Other values (78) 37040
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17885
14.7%
e 11900
 
9.8%
i 9674
 
8.0%
n 7922
 
6.5%
t 7902
 
6.5%
a 7680
 
6.3%
o 6261
 
5.1%
s 5907
 
4.9%
r 5213
 
4.3%
c 4236
 
3.5%
Other values (78) 37040
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17885
14.7%
e 11900
 
9.8%
i 9674
 
8.0%
n 7922
 
6.5%
t 7902
 
6.5%
a 7680
 
6.3%
o 6261
 
5.1%
s 5907
 
4.9%
r 5213
 
4.3%
c 4236
 
3.5%
Other values (78) 37040
30.5%

Contains_Numbers
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Yes
471 
No
459 
69 

Length

Max length3
Median length2
Mean length2.3333333
Min length0

Characters and Unicode

Total characters2331
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 471
47.1%
No 459
45.9%
69
 
6.9%

Length

2025-07-03T06:21:07.354087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:07.426386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 471
50.6%
no 459
49.4%

Most occurring characters

ValueCountFrequency (%)
Y 471
20.2%
e 471
20.2%
s 471
20.2%
N 459
19.7%
o 459
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2331
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 471
20.2%
e 471
20.2%
s 471
20.2%
N 459
19.7%
o 459
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2331
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 471
20.2%
e 471
20.2%
s 471
20.2%
N 459
19.7%
o 459
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2331
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 471
20.2%
e 471
20.2%
s 471
20.2%
N 459
19.7%
o 459
19.7%

Contains_Quotes
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
800 
Yes
130 
 
69

Length

Max length3
Median length2
Mean length1.991992
Min length0

Characters and Unicode

Total characters1990
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 800
80.1%
Yes 130
 
13.0%
69
 
6.9%

Length

2025-07-03T06:21:07.509567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:07.575506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 800
86.0%
yes 130
 
14.0%

Most occurring characters

ValueCountFrequency (%)
N 800
40.2%
o 800
40.2%
Y 130
 
6.5%
e 130
 
6.5%
s 130
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 800
40.2%
o 800
40.2%
Y 130
 
6.5%
e 130
 
6.5%
s 130
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 800
40.2%
o 800
40.2%
Y 130
 
6.5%
e 130
 
6.5%
s 130
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 800
40.2%
o 800
40.2%
Y 130
 
6.5%
e 130
 
6.5%
s 130
 
6.5%

Contains_Question_Mark
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
No
896 
 
69
Yes
 
34

Length

Max length3
Median length2
Mean length1.8958959
Min length0

Characters and Unicode

Total characters1894
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 896
89.7%
69
 
6.9%
Yes 34
 
3.4%

Length

2025-07-03T06:21:07.659921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:07.724419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 896
96.3%
yes 34
 
3.7%

Most occurring characters

ValueCountFrequency (%)
N 896
47.3%
o 896
47.3%
Y 34
 
1.8%
e 34
 
1.8%
s 34
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 896
47.3%
o 896
47.3%
Y 34
 
1.8%
e 34
 
1.8%
s 34
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 896
47.3%
o 896
47.3%
Y 34
 
1.8%
e 34
 
1.8%
s 34
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 896
47.3%
o 896
47.3%
Y 34
 
1.8%
e 34
 
1.8%
s 34
 
1.8%

Contains_Colon
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
808 
Yes
122 
 
69

Length

Max length3
Median length2
Mean length1.983984
Min length0

Characters and Unicode

Total characters1982
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 808
80.9%
Yes 122
 
12.2%
69
 
6.9%

Length

2025-07-03T06:21:07.804108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:07.873296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 808
86.9%
yes 122
 
13.1%

Most occurring characters

ValueCountFrequency (%)
N 808
40.8%
o 808
40.8%
Y 122
 
6.2%
e 122
 
6.2%
s 122
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 808
40.8%
o 808
40.8%
Y 122
 
6.2%
e 122
 
6.2%
s 122
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 808
40.8%
o 808
40.8%
Y 122
 
6.2%
e 122
 
6.2%
s 122
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 808
40.8%
o 808
40.8%
Y 122
 
6.2%
e 122
 
6.2%
s 122
 
6.2%

Contains_Hyphen
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
No
684 
Yes
246 
69 

Length

Max length3
Median length2
Mean length2.1081081
Min length0

Characters and Unicode

Total characters2106
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 684
68.5%
Yes 246
 
24.6%
69
 
6.9%

Length

2025-07-03T06:21:07.959169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:08.026769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 684
73.5%
yes 246
 
26.5%

Most occurring characters

ValueCountFrequency (%)
N 684
32.5%
o 684
32.5%
Y 246
 
11.7%
e 246
 
11.7%
s 246
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 684
32.5%
o 684
32.5%
Y 246
 
11.7%
e 246
 
11.7%
s 246
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 684
32.5%
o 684
32.5%
Y 246
 
11.7%
e 246
 
11.7%
s 246
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 684
32.5%
o 684
32.5%
Y 246
 
11.7%
e 246
 
11.7%
s 246
 
11.7%

Contains_Exclamation_Mark
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
No
927 
 
69
Yes
 
3

Length

Max length3
Median length2
Mean length1.8648649
Min length0

Characters and Unicode

Total characters1863
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 927
92.8%
69
 
6.9%
Yes 3
 
0.3%

Length

2025-07-03T06:21:08.107159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:08.184749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 927
99.7%
yes 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N 927
49.8%
o 927
49.8%
Y 3
 
0.2%
e 3
 
0.2%
s 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 927
49.8%
o 927
49.8%
Y 3
 
0.2%
e 3
 
0.2%
s 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 927
49.8%
o 927
49.8%
Y 3
 
0.2%
e 3
 
0.2%
s 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 927
49.8%
o 927
49.8%
Y 3
 
0.2%
e 3
 
0.2%
s 3
 
0.2%

Starts_With_Number
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
No
848 
Yes
 
82
 
69

Length

Max length3
Median length2
Mean length1.9439439
Min length0

Characters and Unicode

Total characters1942
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 848
84.9%
Yes 82
 
8.2%
69
 
6.9%

Length

2025-07-03T06:21:08.263932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:08.331470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 848
91.2%
yes 82
 
8.8%

Most occurring characters

ValueCountFrequency (%)
N 848
43.7%
o 848
43.7%
Y 82
 
4.2%
e 82
 
4.2%
s 82
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 848
43.7%
o 848
43.7%
Y 82
 
4.2%
e 82
 
4.2%
s 82
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 848
43.7%
o 848
43.7%
Y 82
 
4.2%
e 82
 
4.2%
s 82
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 848
43.7%
o 848
43.7%
Y 82
 
4.2%
e 82
 
4.2%
s 82
 
4.2%

Ends_With_Question_Mark
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
No
908 
 
69
Yes
 
22

Length

Max length3
Median length2
Mean length1.8838839
Min length0

Characters and Unicode

Total characters1882
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 908
90.9%
69
 
6.9%
Yes 22
 
2.2%

Length

2025-07-03T06:21:08.413303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:08.477020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 908
97.6%
yes 22
 
2.4%

Most occurring characters

ValueCountFrequency (%)
N 908
48.2%
o 908
48.2%
Y 22
 
1.2%
e 22
 
1.2%
s 22
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 908
48.2%
o 908
48.2%
Y 22
 
1.2%
e 22
 
1.2%
s 22
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 908
48.2%
o 908
48.2%
Y 22
 
1.2%
e 22
 
1.2%
s 22
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 908
48.2%
o 908
48.2%
Y 22
 
1.2%
e 22
 
1.2%
s 22
 
1.2%

Length_General_Assessment
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size64.1 KiB
Adequate
876 
 
69
Too long, risk of truncation
 
54

Length

Max length28
Median length8
Mean length8.5285285
Min length0

Characters and Unicode

Total characters8520
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdequate
2nd rowAdequate
3rd rowAdequate
4th rowAdequate
5th rowAdequate

Common Values

ValueCountFrequency (%)
Adequate 876
87.7%
69
 
6.9%
Too long, risk of truncation 54
 
5.4%

Length

2025-07-03T06:21:08.552435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:08.616368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
adequate 876
76.4%
too 54
 
4.7%
long 54
 
4.7%
risk 54
 
4.7%
of 54
 
4.7%
truncation 54
 
4.7%

Most occurring characters

ValueCountFrequency (%)
e 1752
20.6%
t 984
11.5%
u 930
10.9%
a 930
10.9%
q 876
10.3%
d 876
10.3%
A 876
10.3%
o 270
 
3.2%
216
 
2.5%
n 162
 
1.9%
Other values (10) 648
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1752
20.6%
t 984
11.5%
u 930
10.9%
a 930
10.9%
q 876
10.3%
d 876
10.3%
A 876
10.3%
o 270
 
3.2%
216
 
2.5%
n 162
 
1.9%
Other values (10) 648
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1752
20.6%
t 984
11.5%
u 930
10.9%
a 930
10.9%
q 876
10.3%
d 876
10.3%
A 876
10.3%
o 270
 
3.2%
216
 
2.5%
n 162
 
1.9%
Other values (10) 648
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1752
20.6%
t 984
11.5%
u 930
10.9%
a 930
10.9%
q 876
10.3%
d 876
10.3%
A 876
10.3%
o 270
 
3.2%
216
 
2.5%
n 162
 
1.9%
Other values (10) 648
 
7.6%
Distinct127
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
2025-07-03T06:21:08.897642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.007007
Min length0

Characters and Unicode

Total characters2005
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)3.5%

Sample

1st row105
2nd row101
3rd row106
4th row173
5th row57
ValueCountFrequency (%)
59 44
 
4.7%
70 28
 
3.0%
60 27
 
2.9%
58 26
 
2.8%
69 26
 
2.8%
50 23
 
2.5%
55 23
 
2.5%
67 22
 
2.4%
104 22
 
2.4%
75 21
 
2.3%
Other values (116) 668
71.8%
2025-07-03T06:21:09.309922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 316
15.8%
6 264
13.2%
1 248
12.4%
9 233
11.6%
7 205
10.2%
0 196
9.8%
4 185
9.2%
8 180
9.0%
3 95
 
4.7%
2 83
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 316
15.8%
6 264
13.2%
1 248
12.4%
9 233
11.6%
7 205
10.2%
0 196
9.8%
4 185
9.2%
8 180
9.0%
3 95
 
4.7%
2 83
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 316
15.8%
6 264
13.2%
1 248
12.4%
9 233
11.6%
7 205
10.2%
0 196
9.8%
4 185
9.2%
8 180
9.0%
3 95
 
4.7%
2 83
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 316
15.8%
6 264
13.2%
1 248
12.4%
9 233
11.6%
7 205
10.2%
0 196
9.8%
4 185
9.2%
8 180
9.0%
3 95
 
4.7%
2 83
 
4.1%
Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
2025-07-03T06:21:09.592568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length183
Median length2
Mean length5.2052052
Min length0

Characters and Unicode

Total characters5200
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)5.8%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 873
63.8%
yes 58
 
4.2%
justified 29
 
2.1%
use 29
 
2.1%
impact 29
 
2.1%
seeks 28
 
2.0%
the 11
 
0.8%
for 11
 
0.8%
and 8
 
0.6%
a 8
 
0.6%
Other values (230) 285
 
20.8%
2025-07-03T06:21:10.018035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 973
18.7%
N 884
17.0%
439
 
8.4%
e 358
 
6.9%
s 266
 
5.1%
' 248
 
4.8%
i 211
 
4.1%
, 187
 
3.6%
t 156
 
3.0%
a 145
 
2.8%
Other values (56) 1333
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 973
18.7%
N 884
17.0%
439
 
8.4%
e 358
 
6.9%
s 266
 
5.1%
' 248
 
4.8%
i 211
 
4.1%
, 187
 
3.6%
t 156
 
3.0%
a 145
 
2.8%
Other values (56) 1333
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 973
18.7%
N 884
17.0%
439
 
8.4%
e 358
 
6.9%
s 266
 
5.1%
' 248
 
4.8%
i 211
 
4.1%
, 187
 
3.6%
t 156
 
3.0%
a 145
 
2.8%
Other values (56) 1333
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 973
18.7%
N 884
17.0%
439
 
8.4%
e 358
 
6.9%
s 266
 
5.1%
' 248
 
4.8%
i 211
 
4.1%
, 187
 
3.6%
t 156
 
3.0%
a 145
 
2.8%
Other values (56) 1333
25.6%

Main_Classification
Categorical

High correlation 

Distinct28
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size73.2 KiB
Declarative Simple
565 
Mystery/Revelation
82 
69 
Attribution ('according to', 'reveals')
 
46
List/Numbered
 
39
Other values (23)
198 

Length

Max length52
Median length18
Mean length17.92993
Min length0

Characters and Unicode

Total characters17912
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.8%

Sample

1st rowDeclarative Simple
2nd rowDeclarative Simple
3rd rowDeclarative Simple
4th rowUrgency
5th rowDeclarative Simple

Common Values

ValueCountFrequency (%)
Declarative Simple 565
56.6%
Mystery/Revelation 82
 
8.2%
69
 
6.9%
Attribution ('according to', 'reveals') 46
 
4.6%
List/Numbered 39
 
3.9%
List/Numbered ('5 ways') 33
 
3.3%
Direct Question 32
 
3.2%
Direct Quote 29
 
2.9%
Superlative ('best', 'worst') 20
 
2.0%
Mystery/Revelation ('secret', 'truth') 14
 
1.4%
Other values (18) 70
 
7.0%

Length

2025-07-03T06:21:10.147279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
declarative 566
30.1%
simple 565
30.1%
mystery/revelation 96
 
5.1%
direct 76
 
4.0%
list/numbered 75
 
4.0%
according 49
 
2.6%
to 49
 
2.6%
reveals 49
 
2.6%
attribution 49
 
2.6%
question 42
 
2.2%
Other values (23) 263
14.0%

Most occurring characters

ValueCountFrequency (%)
e 2560
14.3%
i 1662
 
9.3%
a 1426
 
8.0%
t 1388
 
7.7%
l 1316
 
7.3%
r 1096
 
6.1%
949
 
5.3%
c 773
 
4.3%
v 763
 
4.3%
m 655
 
3.7%
Other values (33) 5324
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2560
14.3%
i 1662
 
9.3%
a 1426
 
8.0%
t 1388
 
7.7%
l 1316
 
7.3%
r 1096
 
6.1%
949
 
5.3%
c 773
 
4.3%
v 763
 
4.3%
m 655
 
3.7%
Other values (33) 5324
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2560
14.3%
i 1662
 
9.3%
a 1426
 
8.0%
t 1388
 
7.7%
l 1316
 
7.3%
r 1096
 
6.1%
949
 
5.3%
c 773
 
4.3%
v 763
 
4.3%
m 655
 
3.7%
Other values (33) 5324
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2560
14.3%
i 1662
 
9.3%
a 1426
 
8.0%
t 1388
 
7.7%
l 1316
 
7.3%
r 1096
 
6.1%
949
 
5.3%
c 773
 
4.3%
v 763
 
4.3%
m 655
 
3.7%
Other values (33) 5324
29.7%
Distinct928
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size175.6 KiB
2025-07-03T06:21:10.434839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length333
Median length178
Mean length118.71171
Min length0

Characters and Unicode

Total characters118593
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique925 ?
Unique (%)92.6%

Sample

1st rowThe headline presents a series of factual statements about Ford's situation and the CEO's admission without posing a question, using urgency markers, or comparing elements.
2nd rowThe headline directly states a new rule and its implications, serving as a straightforward announcement.
3rd rowThe headline makes a direct statement about an event, presenting it as a fact without using a question, direct quote, or emphasizing urgency.
4th rowThe headline clearly states a new policy confirmed by an authority and strongly emphasizes the immediate, widespread need for action, particularly with the phrase 'renew urgently' affecting 'millions of drivers'.
5th rowThe headline directly states a fact without posing a question, using a quote, or indicating urgency beyond the date.
ValueCountFrequency (%)
a 1778
 
9.9%
the 1441
 
8.0%
headline 880
 
4.9%
or 572
 
3.2%
without 456
 
2.5%
question 364
 
2.0%
and 356
 
2.0%
statement 356
 
2.0%
about 329
 
1.8%
an 315
 
1.8%
Other values (1644) 11106
61.9%
2025-07-03T06:21:10.875255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17023
14.4%
e 12052
 
10.2%
t 10131
 
8.5%
a 8796
 
7.4%
i 8657
 
7.3%
n 7590
 
6.4%
s 6141
 
5.2%
o 5778
 
4.9%
r 5404
 
4.6%
h 3889
 
3.3%
Other values (74) 33132
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 118593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17023
14.4%
e 12052
 
10.2%
t 10131
 
8.5%
a 8796
 
7.4%
i 8657
 
7.3%
n 7590
 
6.4%
s 6141
 
5.2%
o 5778
 
4.9%
r 5404
 
4.6%
h 3889
 
3.3%
Other values (74) 33132
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 118593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17023
14.4%
e 12052
 
10.2%
t 10131
 
8.5%
a 8796
 
7.4%
i 8657
 
7.3%
n 7590
 
6.4%
s 6141
 
5.2%
o 5778
 
4.9%
r 5404
 
4.6%
h 3889
 
3.3%
Other values (74) 33132
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 118593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17023
14.4%
e 12052
 
10.2%
t 10131
 
8.5%
a 8796
 
7.4%
i 8657
 
7.3%
n 7590
 
6.4%
s 6141
 
5.2%
o 5778
 
4.9%
r 5404
 
4.6%
h 3889
 
3.3%
Other values (74) 33132
27.9%

Temporal_Urgency_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
801 
Yes
129 
 
69

Length

Max length3
Median length2
Mean length1.990991
Min length0

Characters and Unicode

Total characters1989
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 801
80.2%
Yes 129
 
12.9%
69
 
6.9%

Length

2025-07-03T06:21:10.991978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:11.058841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 801
86.1%
yes 129
 
13.9%

Most occurring characters

ValueCountFrequency (%)
N 801
40.3%
o 801
40.3%
Y 129
 
6.5%
e 129
 
6.5%
s 129
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1989
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 801
40.3%
o 801
40.3%
Y 129
 
6.5%
e 129
 
6.5%
s 129
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1989
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 801
40.3%
o 801
40.3%
Y 129
 
6.5%
e 129
 
6.5%
s 129
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1989
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 801
40.3%
o 801
40.3%
Y 129
 
6.5%
e 129
 
6.5%
s 129
 
6.5%
Distinct94
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size59.1 KiB
2025-07-03T06:21:11.193564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length2
Mean length3.4214214
Min length0

Characters and Unicode

Total characters3418
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)7.3%

Sample

1st row["immediately"]
2nd row["Begins July 2025"]
3rd row[]
4th row["immediate","urgently"]
5th row[]
ValueCountFrequency (%)
801
79.8%
this 11
 
1.1%
now 9
 
0.9%
july 8
 
0.8%
just 8
 
0.8%
tonight 6
 
0.6%
1 6
 
0.6%
week 6
 
0.6%
new 6
 
0.6%
immediately 5
 
0.5%
Other values (97) 138
 
13.7%
2025-07-03T06:21:11.511414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 930
27.2%
] 930
27.2%
" 308
 
9.0%
e 147
 
4.3%
t 86
 
2.5%
n 83
 
2.4%
74
 
2.2%
a 70
 
2.0%
i 66
 
1.9%
s 57
 
1.7%
Other values (49) 667
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 930
27.2%
] 930
27.2%
" 308
 
9.0%
e 147
 
4.3%
t 86
 
2.5%
n 83
 
2.4%
74
 
2.2%
a 70
 
2.0%
i 66
 
1.9%
s 57
 
1.7%
Other values (49) 667
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 930
27.2%
] 930
27.2%
" 308
 
9.0%
e 147
 
4.3%
t 86
 
2.5%
n 83
 
2.4%
74
 
2.2%
a 70
 
2.0%
i 66
 
1.9%
s 57
 
1.7%
Other values (49) 667
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 930
27.2%
] 930
27.2%
" 308
 
9.0%
e 147
 
4.3%
t 86
 
2.5%
n 83
 
2.4%
74
 
2.2%
a 70
 
2.0%
i 66
 
1.9%
s 57
 
1.7%
Other values (49) 667
19.5%

Exclusivity_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
No
897 
 
69
Yes
 
33

Length

Max length3
Median length2
Mean length1.8948949
Min length0

Characters and Unicode

Total characters1893
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 897
89.8%
69
 
6.9%
Yes 33
 
3.3%

Length

2025-07-03T06:21:11.621923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:11.689209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 897
96.5%
yes 33
 
3.5%

Most occurring characters

ValueCountFrequency (%)
N 897
47.4%
o 897
47.4%
Y 33
 
1.7%
e 33
 
1.7%
s 33
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 897
47.4%
o 897
47.4%
Y 33
 
1.7%
e 33
 
1.7%
s 33
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 897
47.4%
o 897
47.4%
Y 33
 
1.7%
e 33
 
1.7%
s 33
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 897
47.4%
o 897
47.4%
Y 33
 
1.7%
e 33
 
1.7%
s 33
 
1.7%

Exclusivity_Words
Categorical

High correlation  Imbalance 

Distinct28
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
[]
897 
 
69
["Rare"]
 
3
["first"]
 
2
["only"]
 
2
Other values (23)
 
26

Length

Max length35
Median length2
Mean length2.2352352
Min length0

Characters and Unicode

Total characters2233
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)2.0%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]

Common Values

ValueCountFrequency (%)
[] 897
89.8%
69
 
6.9%
["Rare"] 3
 
0.3%
["first"] 2
 
0.2%
["only"] 2
 
0.2%
["First"] 2
 
0.2%
["Only"] 2
 
0.2%
["Limited-Edition"] 2
 
0.2%
["Last Surviving","Rare"] 1
 
0.1%
["first","never before seen"] 1
 
0.1%
Other values (18) 18
 
1.8%

Length

2025-07-03T06:21:11.780408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
897
95.0%
first 6
 
0.6%
only 5
 
0.5%
rare 4
 
0.4%
limited-edition 2
 
0.2%
unique 2
 
0.2%
surviving","rare 1
 
0.1%
first","never 1
 
0.1%
before 1
 
0.1%
last 1
 
0.1%
Other values (24) 24
 
2.5%

Most occurring characters

ValueCountFrequency (%)
[ 930
41.6%
] 930
41.6%
" 72
 
3.2%
e 39
 
1.7%
i 29
 
1.3%
t 24
 
1.1%
r 21
 
0.9%
n 21
 
0.9%
s 16
 
0.7%
14
 
0.6%
Other values (35) 137
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 930
41.6%
] 930
41.6%
" 72
 
3.2%
e 39
 
1.7%
i 29
 
1.3%
t 24
 
1.1%
r 21
 
0.9%
n 21
 
0.9%
s 16
 
0.7%
14
 
0.6%
Other values (35) 137
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 930
41.6%
] 930
41.6%
" 72
 
3.2%
e 39
 
1.7%
i 29
 
1.3%
t 24
 
1.1%
r 21
 
0.9%
n 21
 
0.9%
s 16
 
0.7%
14
 
0.6%
Other values (35) 137
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 930
41.6%
] 930
41.6%
" 72
 
3.2%
e 39
 
1.7%
i 29
 
1.3%
t 24
 
1.1%
r 21
 
0.9%
n 21
 
0.9%
s 16
 
0.7%
14
 
0.6%
Other values (35) 137
 
6.1%

Authority_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
No
699 
Yes
231 
 
69

Length

Max length3
Median length2
Mean length2.0930931
Min length0

Characters and Unicode

Total characters2091
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 699
70.0%
Yes 231
 
23.1%
69
 
6.9%

Length

2025-07-03T06:21:11.896338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:11.965224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 699
75.2%
yes 231
 
24.8%

Most occurring characters

ValueCountFrequency (%)
N 699
33.4%
o 699
33.4%
Y 231
 
11.0%
e 231
 
11.0%
s 231
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2091
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 699
33.4%
o 699
33.4%
Y 231
 
11.0%
e 231
 
11.0%
s 231
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2091
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 699
33.4%
o 699
33.4%
Y 231
 
11.0%
e 231
 
11.0%
s 231
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2091
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 699
33.4%
o 699
33.4%
Y 231
 
11.0%
e 231
 
11.0%
s 231
 
11.0%
Distinct198
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
2025-07-03T06:21:12.131283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length55
Median length2
Mean length5.4384384
Min length0

Characters and Unicode

Total characters5433
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)17.5%

Sample

1st row["CEO admits"]
2nd row[]
3rd row[]
4th row["DMV","confirms"]
5th row[]
ValueCountFrequency (%)
699
67.3%
experts 9
 
0.9%
social 7
 
0.7%
tsa 6
 
0.6%
law 6
 
0.6%
scientists 6
 
0.6%
to 6
 
0.6%
security 5
 
0.5%
officials 5
 
0.5%
confirms 4
 
0.4%
Other values (240) 286
27.5%
2025-07-03T06:21:12.490777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 930
17.1%
] 930
17.1%
" 652
12.0%
e 252
 
4.6%
i 203
 
3.7%
s 197
 
3.6%
o 196
 
3.6%
r 194
 
3.6%
a 176
 
3.2%
t 170
 
3.1%
Other values (48) 1533
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5433
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 930
17.1%
] 930
17.1%
" 652
12.0%
e 252
 
4.6%
i 203
 
3.7%
s 197
 
3.6%
o 196
 
3.6%
r 194
 
3.6%
a 176
 
3.2%
t 170
 
3.1%
Other values (48) 1533
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5433
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 930
17.1%
] 930
17.1%
" 652
12.0%
e 252
 
4.6%
i 203
 
3.7%
s 197
 
3.6%
o 196
 
3.6%
r 194
 
3.6%
a 176
 
3.2%
t 170
 
3.1%
Other values (48) 1533
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5433
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 930
17.1%
] 930
17.1%
" 652
12.0%
e 252
 
4.6%
i 203
 
3.7%
s 197
 
3.6%
o 196
 
3.6%
r 194
 
3.6%
a 176
 
3.2%
t 170
 
3.1%
Other values (48) 1533
28.2%

Solution_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
773 
Yes
157 
 
69

Length

Max length3
Median length2
Mean length2.019019
Min length0

Characters and Unicode

Total characters2017
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 773
77.4%
Yes 157
 
15.7%
69
 
6.9%

Length

2025-07-03T06:21:12.607234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:12.682356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 773
83.1%
yes 157
 
16.9%

Most occurring characters

ValueCountFrequency (%)
N 773
38.3%
o 773
38.3%
Y 157
 
7.8%
e 157
 
7.8%
s 157
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 773
38.3%
o 773
38.3%
Y 157
 
7.8%
e 157
 
7.8%
s 157
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 773
38.3%
o 773
38.3%
Y 157
 
7.8%
e 157
 
7.8%
s 157
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 773
38.3%
o 773
38.3%
Y 157
 
7.8%
e 157
 
7.8%
s 157
 
7.8%
Distinct143
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size59.7 KiB
2025-07-03T06:21:12.872753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length2
Mean length4.1061061
Min length0

Characters and Unicode

Total characters4102
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)13.0%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]
ValueCountFrequency (%)
774
73.7%
to 23
 
2.2%
how 18
 
1.7%
best 8
 
0.8%
way 5
 
0.5%
watch 4
 
0.4%
clean 4
 
0.4%
easy 4
 
0.4%
restored 3
 
0.3%
it 3
 
0.3%
Other values (180) 204
 
19.4%
2025-07-03T06:21:13.222148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 930
22.7%
] 930
22.7%
" 412
10.0%
e 231
 
5.6%
o 138
 
3.4%
t 136
 
3.3%
120
 
2.9%
i 93
 
2.3%
r 89
 
2.2%
a 86
 
2.1%
Other values (46) 937
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 930
22.7%
] 930
22.7%
" 412
10.0%
e 231
 
5.6%
o 138
 
3.4%
t 136
 
3.3%
120
 
2.9%
i 93
 
2.3%
r 89
 
2.2%
a 86
 
2.1%
Other values (46) 937
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 930
22.7%
] 930
22.7%
" 412
10.0%
e 231
 
5.6%
o 138
 
3.4%
t 136
 
3.3%
120
 
2.9%
i 93
 
2.3%
r 89
 
2.2%
a 86
 
2.1%
Other values (46) 937
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 930
22.7%
] 930
22.7%
" 412
10.0%
e 231
 
5.6%
o 138
 
3.4%
t 136
 
3.3%
120
 
2.9%
i 93
 
2.3%
r 89
 
2.2%
a 86
 
2.1%
Other values (46) 937
22.8%

Economic_Benefit_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
790 
Yes
140 
 
69

Length

Max length3
Median length2
Mean length2.002002
Min length0

Characters and Unicode

Total characters2000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 790
79.1%
Yes 140
 
14.0%
69
 
6.9%

Length

2025-07-03T06:21:13.345065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:13.421237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 790
84.9%
yes 140
 
15.1%

Most occurring characters

ValueCountFrequency (%)
N 790
39.5%
o 790
39.5%
Y 140
 
7.0%
e 140
 
7.0%
s 140
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 790
39.5%
o 790
39.5%
Y 140
 
7.0%
e 140
 
7.0%
s 140
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 790
39.5%
o 790
39.5%
Y 140
 
7.0%
e 140
 
7.0%
s 140
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 790
39.5%
o 790
39.5%
Y 140
 
7.0%
e 140
 
7.0%
s 140
 
7.0%
Distinct134
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
2025-07-03T06:21:13.586966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length2
Mean length4.4494494
Min length0

Characters and Unicode

Total characters4445
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)12.4%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]
ValueCountFrequency (%)
790
76.6%
million 16
 
1.6%
refunds 4
 
0.4%
benefits 4
 
0.4%
free 3
 
0.3%
valuable 3
 
0.3%
billion 3
 
0.3%
fines 3
 
0.3%
tax 3
 
0.3%
affordable 2
 
0.2%
Other values (182) 200
 
19.4%
2025-07-03T06:21:13.929827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 930
20.9%
] 930
20.9%
" 464
 
10.4%
e 174
 
3.9%
l 147
 
3.3%
i 147
 
3.3%
n 132
 
3.0%
, 113
 
2.5%
o 112
 
2.5%
101
 
2.3%
Other values (58) 1195
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 930
20.9%
] 930
20.9%
" 464
 
10.4%
e 174
 
3.9%
l 147
 
3.3%
i 147
 
3.3%
n 132
 
3.0%
, 113
 
2.5%
o 112
 
2.5%
101
 
2.3%
Other values (58) 1195
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 930
20.9%
] 930
20.9%
" 464
 
10.4%
e 174
 
3.9%
l 147
 
3.3%
i 147
 
3.3%
n 132
 
3.0%
, 113
 
2.5%
o 112
 
2.5%
101
 
2.3%
Other values (58) 1195
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 930
20.9%
] 930
20.9%
" 464
 
10.4%
e 174
 
3.9%
l 147
 
3.3%
i 147
 
3.3%
n 132
 
3.0%
, 113
 
2.5%
o 112
 
2.5%
101
 
2.3%
Other values (58) 1195
26.9%

Prohibition_Restriction_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
No
865 
 
69
Yes
 
65

Length

Max length3
Median length2
Mean length1.9269269
Min length0

Characters and Unicode

Total characters1925
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 865
86.6%
69
 
6.9%
Yes 65
 
6.5%

Length

2025-07-03T06:21:14.044894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:14.560359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 865
93.0%
yes 65
 
7.0%

Most occurring characters

ValueCountFrequency (%)
N 865
44.9%
o 865
44.9%
Y 65
 
3.4%
e 65
 
3.4%
s 65
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 865
44.9%
o 865
44.9%
Y 65
 
3.4%
e 65
 
3.4%
s 65
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 865
44.9%
o 865
44.9%
Y 65
 
3.4%
e 65
 
3.4%
s 65
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 865
44.9%
o 865
44.9%
Y 65
 
3.4%
e 65
 
3.4%
s 65
 
3.4%
Distinct58
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
2025-07-03T06:21:14.821860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length68
Median length2
Mean length2.987988
Min length0

Characters and Unicode

Total characters2985
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)4.9%

Sample

1st row[]
2nd row[]
3rd row["denied"]
4th row["ending the benefit","without immediate renewal"]
5th row[]
ValueCountFrequency (%)
865
86.2%
to 8
 
0.8%
not 5
 
0.5%
avoid 4
 
0.4%
away 3
 
0.3%
banned 3
 
0.3%
ban 3
 
0.3%
banning 2
 
0.2%
neither 2
 
0.2%
no 2
 
0.2%
Other values (98) 106
 
10.6%
2025-07-03T06:21:15.335842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 930
31.2%
] 930
31.2%
" 174
 
5.8%
e 98
 
3.3%
o 74
 
2.5%
73
 
2.4%
n 72
 
2.4%
i 69
 
2.3%
t 63
 
2.1%
r 50
 
1.7%
Other values (40) 452
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 930
31.2%
] 930
31.2%
" 174
 
5.8%
e 98
 
3.3%
o 74
 
2.5%
73
 
2.4%
n 72
 
2.4%
i 69
 
2.3%
t 63
 
2.1%
r 50
 
1.7%
Other values (40) 452
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 930
31.2%
] 930
31.2%
" 174
 
5.8%
e 98
 
3.3%
o 74
 
2.5%
73
 
2.4%
n 72
 
2.4%
i 69
 
2.3%
t 63
 
2.1%
r 50
 
1.7%
Other values (40) 452
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 930
31.2%
] 930
31.2%
" 174
 
5.8%
e 98
 
3.3%
o 74
 
2.5%
73
 
2.4%
n 72
 
2.4%
i 69
 
2.3%
t 63
 
2.1%
r 50
 
1.7%
Other values (40) 452
15.1%

National_Relevance_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
No
562 
Yes
368 
69 

Length

Max length3
Median length2
Mean length2.2302302
Min length0

Characters and Unicode

Total characters2228
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 562
56.3%
Yes 368
36.8%
69
 
6.9%

Length

2025-07-03T06:21:15.522947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:15.614417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 562
60.4%
yes 368
39.6%

Most occurring characters

ValueCountFrequency (%)
N 562
25.2%
o 562
25.2%
Y 368
16.5%
e 368
16.5%
s 368
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 562
25.2%
o 562
25.2%
Y 368
16.5%
e 368
16.5%
s 368
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 562
25.2%
o 562
25.2%
Y 368
16.5%
e 368
16.5%
s 368
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 562
25.2%
o 562
25.2%
Y 368
16.5%
e 368
16.5%
s 368
16.5%
Distinct222
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
2025-07-03T06:21:15.807936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length123
Median length2
Mean length7.8978979
Min length0

Characters and Unicode

Total characters7890
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique185 ?
Unique (%)18.5%

Sample

1st row[]
2nd row["U.S."]
3rd row[]
4th row["United States"]
5th row[]
ValueCountFrequency (%)
562
50.4%
new 35
 
3.1%
york 33
 
3.0%
california 26
 
2.3%
states 21
 
1.9%
texas 15
 
1.3%
state 15
 
1.3%
us 14
 
1.3%
illinois 11
 
1.0%
u.s 10
 
0.9%
Other values (280) 373
33.5%
2025-07-03T06:21:16.315743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 1170
14.8%
[ 930
 
11.8%
] 930
 
11.8%
a 460
 
5.8%
i 361
 
4.6%
e 352
 
4.5%
n 306
 
3.9%
o 295
 
3.7%
t 281
 
3.6%
r 267
 
3.4%
Other values (49) 2538
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 1170
14.8%
[ 930
 
11.8%
] 930
 
11.8%
a 460
 
5.8%
i 361
 
4.6%
e 352
 
4.5%
n 306
 
3.9%
o 295
 
3.7%
t 281
 
3.6%
r 267
 
3.4%
Other values (49) 2538
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 1170
14.8%
[ 930
 
11.8%
] 930
 
11.8%
a 460
 
5.8%
i 361
 
4.6%
e 352
 
4.5%
n 306
 
3.9%
o 295
 
3.7%
t 281
 
3.6%
r 267
 
3.4%
Other values (49) 2538
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 1170
14.8%
[ 930
 
11.8%
] 930
 
11.8%
a 460
 
5.8%
i 361
 
4.6%
e 352
 
4.5%
n 306
 
3.9%
o 295
 
3.7%
t 281
 
3.6%
r 267
 
3.4%
Other values (49) 2538
32.2%

Recognized_Brand_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
No
595 
Yes
335 
69 

Length

Max length3
Median length2
Mean length2.1971972
Min length0

Characters and Unicode

Total characters2195
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No 595
59.6%
Yes 335
33.5%
69
 
6.9%

Length

2025-07-03T06:21:16.497678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:16.608030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 595
64.0%
yes 335
36.0%

Most occurring characters

ValueCountFrequency (%)
N 595
27.1%
o 595
27.1%
Y 335
15.3%
e 335
15.3%
s 335
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 595
27.1%
o 595
27.1%
Y 335
15.3%
e 335
15.3%
s 335
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 595
27.1%
o 595
27.1%
Y 335
15.3%
e 335
15.3%
s 335
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 595
27.1%
o 595
27.1%
Y 335
15.3%
e 335
15.3%
s 335
15.3%
Distinct262
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size64.1 KiB
2025-07-03T06:21:16.828693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length154
Median length2
Mean length8.0750751
Min length0

Characters and Unicode

Total characters8067
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)22.3%

Sample

1st row["Ford"]
2nd row[]
3rd row[]
4th row["DMV"]
5th row["McDonald's"]
ValueCountFrequency (%)
597
48.5%
security 14
 
1.1%
social 13
 
1.1%
walmart 11
 
0.9%
air 8
 
0.6%
tsa 7
 
0.6%
elon 5
 
0.4%
dollar 5
 
0.4%
webb 5
 
0.4%
mcdonald's 5
 
0.4%
Other values (442) 561
45.6%
2025-07-03T06:21:17.205365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 1024
 
12.7%
[ 930
 
11.5%
] 930
 
11.5%
e 446
 
5.5%
a 392
 
4.9%
r 344
 
4.3%
301
 
3.7%
i 292
 
3.6%
o 283
 
3.5%
l 248
 
3.1%
Other values (62) 2877
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 1024
 
12.7%
[ 930
 
11.5%
] 930
 
11.5%
e 446
 
5.5%
a 392
 
4.9%
r 344
 
4.3%
301
 
3.7%
i 292
 
3.6%
o 283
 
3.5%
l 248
 
3.1%
Other values (62) 2877
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 1024
 
12.7%
[ 930
 
11.5%
] 930
 
11.5%
e 446
 
5.5%
a 392
 
4.9%
r 344
 
4.3%
301
 
3.7%
i 292
 
3.6%
o 283
 
3.5%
l 248
 
3.1%
Other values (62) 2877
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 1024
 
12.7%
[ 930
 
11.5%
] 930
 
11.5%
e 446
 
5.5%
a 392
 
4.9%
r 344
 
4.3%
301
 
3.7%
i 292
 
3.6%
o 283
 
3.5%
l 248
 
3.1%
Other values (62) 2877
35.7%

Curiosity_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.3 KiB
Yes
725 
No
205 
 
69

Length

Max length3
Median length3
Mean length2.5875876
Min length0

Characters and Unicode

Total characters2585
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 725
72.6%
No 205
 
20.5%
69
 
6.9%

Length

2025-07-03T06:21:17.321711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:17.391268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 725
78.0%
no 205
 
22.0%

Most occurring characters

ValueCountFrequency (%)
Y 725
28.0%
e 725
28.0%
s 725
28.0%
N 205
 
7.9%
o 205
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 725
28.0%
e 725
28.0%
s 725
28.0%
N 205
 
7.9%
o 205
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 725
28.0%
e 725
28.0%
s 725
28.0%
N 205
 
7.9%
o 205
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 725
28.0%
e 725
28.0%
s 725
28.0%
N 205
 
7.9%
o 205
 
7.9%
Distinct727
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Memory size170.1 KiB
2025-07-03T06:21:17.724503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length273
Median length207
Mean length101.34034
Min length0

Characters and Unicode

Total characters101239
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique726 ?
Unique (%)72.7%

Sample

1st row
2nd rowThe phrase 'Essential Changes' prompts readers to seek information on what those changes entail.
3rd row
4th row
5th rowThe headline creates an information gap by not revealing which specific breakfast item is being removed.
ValueCountFrequency (%)
the 1676
 
10.4%
and 557
 
3.4%
to 487
 
3.0%
an 460
 
2.8%
gap 457
 
2.8%
information 451
 
2.8%
creates 446
 
2.8%
about 377
 
2.3%
reader 319
 
2.0%
a 295
 
1.8%
Other values (2496) 10623
65.8%
2025-07-03T06:21:18.251269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15422
15.2%
e 9960
 
9.8%
a 7347
 
7.3%
t 6884
 
6.8%
i 6258
 
6.2%
n 5866
 
5.8%
r 5693
 
5.6%
o 5654
 
5.6%
s 5034
 
5.0%
h 3992
 
3.9%
Other values (76) 29129
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101239
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15422
15.2%
e 9960
 
9.8%
a 7347
 
7.3%
t 6884
 
6.8%
i 6258
 
6.2%
n 5866
 
5.8%
r 5693
 
5.6%
o 5654
 
5.6%
s 5034
 
5.0%
h 3992
 
3.9%
Other values (76) 29129
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101239
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15422
15.2%
e 9960
 
9.8%
a 7347
 
7.3%
t 6884
 
6.8%
i 6258
 
6.2%
n 5866
 
5.8%
r 5693
 
5.6%
o 5654
 
5.6%
s 5034
 
5.0%
h 3992
 
3.9%
Other values (76) 29129
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101239
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15422
15.2%
e 9960
 
9.8%
a 7347
 
7.3%
t 6884
 
6.8%
i 6258
 
6.2%
n 5866
 
5.8%
r 5693
 
5.6%
o 5654
 
5.6%
s 5034
 
5.0%
h 3992
 
3.9%
Other values (76) 29129
28.8%

Fear_Concern_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
No
566 
Yes
364 
69 

Length

Max length3
Median length2
Mean length2.2262262
Min length0

Characters and Unicode

Total characters2224
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No 566
56.7%
Yes 364
36.4%
69
 
6.9%

Length

2025-07-03T06:21:18.385909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:18.456337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 566
60.9%
yes 364
39.1%

Most occurring characters

ValueCountFrequency (%)
N 566
25.4%
o 566
25.4%
Y 364
16.4%
e 364
16.4%
s 364
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 566
25.4%
o 566
25.4%
Y 364
16.4%
e 364
16.4%
s 364
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 566
25.4%
o 566
25.4%
Y 364
16.4%
e 364
16.4%
s 364
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 566
25.4%
o 566
25.4%
Y 364
16.4%
e 364
16.4%
s 364
16.4%
Distinct366
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Memory size105.4 KiB
2025-07-03T06:21:18.780815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length240
Median length0
Mean length46.847848
Min length0

Characters and Unicode

Total characters46801
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique365 ?
Unique (%)36.5%

Sample

1st rowThe phrases 'forced to immediately shut down factories', 'halt car production', and 'struggle for brand' evoke concern about economic stability and the future of a major company.
2nd row
3rd row
4th rowThe phrase 'millions of drivers will have to renew urgently' directly implies potential negative consequences or significant inconvenience if the reader fails to act, evoking concern.
5th rowThe phrase 'Removing... for Good' could evoke concern among customers about a favorite item.
ValueCountFrequency (%)
the 481
 
6.9%
and 332
 
4.7%
concern 304
 
4.3%
of 244
 
3.5%
a 199
 
2.8%
or 167
 
2.4%
about 149
 
2.1%
for 148
 
2.1%
evoke 124
 
1.8%
potential 122
 
1.7%
Other values (1577) 4720
67.5%
2025-07-03T06:21:19.288196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6625
14.2%
e 4808
 
10.3%
n 3338
 
7.1%
o 3042
 
6.5%
a 2973
 
6.4%
i 2758
 
5.9%
t 2725
 
5.8%
r 2671
 
5.7%
s 2393
 
5.1%
c 1988
 
4.2%
Other values (68) 13480
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6625
14.2%
e 4808
 
10.3%
n 3338
 
7.1%
o 3042
 
6.5%
a 2973
 
6.4%
i 2758
 
5.9%
t 2725
 
5.8%
r 2671
 
5.7%
s 2393
 
5.1%
c 1988
 
4.2%
Other values (68) 13480
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6625
14.2%
e 4808
 
10.3%
n 3338
 
7.1%
o 3042
 
6.5%
a 2973
 
6.4%
i 2758
 
5.9%
t 2725
 
5.8%
r 2671
 
5.7%
s 2393
 
5.1%
c 1988
 
4.2%
Other values (68) 13480
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6625
14.2%
e 4808
 
10.3%
n 3338
 
7.1%
o 3042
 
6.5%
a 2973
 
6.4%
i 2758
 
5.9%
t 2725
 
5.8%
r 2671
 
5.7%
s 2393
 
5.1%
c 1988
 
4.2%
Other values (68) 13480
28.8%

Surprise_Awe_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
No
615 
Yes
315 
69 

Length

Max length3
Median length2
Mean length2.1771772
Min length0

Characters and Unicode

Total characters2175
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 615
61.6%
Yes 315
31.5%
69
 
6.9%

Length

2025-07-03T06:21:19.409691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:19.487049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 615
66.1%
yes 315
33.9%

Most occurring characters

ValueCountFrequency (%)
N 615
28.3%
o 615
28.3%
Y 315
14.5%
e 315
14.5%
s 315
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 615
28.3%
o 615
28.3%
Y 315
14.5%
e 315
14.5%
s 315
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 615
28.3%
o 615
28.3%
Y 315
14.5%
e 315
14.5%
s 315
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 615
28.3%
o 615
28.3%
Y 315
14.5%
e 315
14.5%
s 315
14.5%
Distinct317
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
2025-07-03T06:21:19.788986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length206
Median length0
Mean length36.406406
Min length0

Characters and Unicode

Total characters36370
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique316 ?
Unique (%)31.6%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
the 500
 
8.6%
of 333
 
5.7%
and 288
 
4.9%
a 279
 
4.8%
surprise 159
 
2.7%
unexpected 102
 
1.8%
is 97
 
1.7%
can 96
 
1.6%
evoke 91
 
1.6%
surprising 90
 
1.5%
Other values (1552) 3791
65.1%
2025-07-03T06:21:20.283648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5510
15.1%
e 3954
 
10.9%
n 2317
 
6.4%
a 2285
 
6.3%
i 2239
 
6.2%
s 2165
 
6.0%
t 1962
 
5.4%
r 1904
 
5.2%
o 1825
 
5.0%
d 1032
 
2.8%
Other values (69) 11177
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5510
15.1%
e 3954
 
10.9%
n 2317
 
6.4%
a 2285
 
6.3%
i 2239
 
6.2%
s 2165
 
6.0%
t 1962
 
5.4%
r 1904
 
5.2%
o 1825
 
5.0%
d 1032
 
2.8%
Other values (69) 11177
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5510
15.1%
e 3954
 
10.9%
n 2317
 
6.4%
a 2285
 
6.3%
i 2239
 
6.2%
s 2165
 
6.0%
t 1962
 
5.4%
r 1904
 
5.2%
o 1825
 
5.0%
d 1032
 
2.8%
Other values (69) 11177
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5510
15.1%
e 3954
 
10.9%
n 2317
 
6.4%
a 2285
 
6.3%
i 2239
 
6.2%
s 2165
 
6.0%
t 1962
 
5.4%
r 1904
 
5.2%
o 1825
 
5.0%
d 1032
 
2.8%
Other values (69) 11177
30.7%

Indignation_Controversy_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
769 
Yes
161 
 
69

Length

Max length3
Median length2
Mean length2.023023
Min length0

Characters and Unicode

Total characters2021
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 769
77.0%
Yes 161
 
16.1%
69
 
6.9%

Length

2025-07-03T06:21:20.407799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:20.476908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 769
82.7%
yes 161
 
17.3%

Most occurring characters

ValueCountFrequency (%)
N 769
38.1%
o 769
38.1%
Y 161
 
8.0%
e 161
 
8.0%
s 161
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 769
38.1%
o 769
38.1%
Y 161
 
8.0%
e 161
 
8.0%
s 161
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 769
38.1%
o 769
38.1%
Y 161
 
8.0%
e 161
 
8.0%
s 161
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 769
38.1%
o 769
38.1%
Y 161
 
8.0%
e 161
 
8.0%
s 161
 
8.0%
Distinct163
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size81.4 KiB
2025-07-03T06:21:20.821397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length264
Median length0
Mean length22.155155
Min length0

Characters and Unicode

Total characters22133
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique162 ?
Unique (%)16.2%

Sample

1st row
2nd row
3rd rowThe phrase 'denied commissions because they’re transgender' directly implies a potential act of discrimination, which is highly likely to provoke indignation and controversy among various audiences.
4th row
5th row
ValueCountFrequency (%)
the 199
 
6.1%
and 156
 
4.8%
a 130
 
4.0%
indignation 111
 
3.4%
of 110
 
3.4%
or 88
 
2.7%
to 80
 
2.4%
provoke 76
 
2.3%
debate 71
 
2.2%
can 66
 
2.0%
Other values (1004) 2179
66.7%
2025-07-03T06:21:21.354082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3104
14.0%
e 1990
 
9.0%
n 1724
 
7.8%
i 1630
 
7.4%
a 1553
 
7.0%
o 1552
 
7.0%
t 1416
 
6.4%
r 1179
 
5.3%
s 1018
 
4.6%
d 776
 
3.5%
Other values (65) 6191
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22133
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3104
14.0%
e 1990
 
9.0%
n 1724
 
7.8%
i 1630
 
7.4%
a 1553
 
7.0%
o 1552
 
7.0%
t 1416
 
6.4%
r 1179
 
5.3%
s 1018
 
4.6%
d 776
 
3.5%
Other values (65) 6191
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22133
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3104
14.0%
e 1990
 
9.0%
n 1724
 
7.8%
i 1630
 
7.4%
a 1553
 
7.0%
o 1552
 
7.0%
t 1416
 
6.4%
r 1179
 
5.3%
s 1018
 
4.6%
d 776
 
3.5%
Other values (65) 6191
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22133
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3104
14.0%
e 1990
 
9.0%
n 1724
 
7.8%
i 1630
 
7.4%
a 1553
 
7.0%
o 1552
 
7.0%
t 1416
 
6.4%
r 1179
 
5.3%
s 1018
 
4.6%
d 776
 
3.5%
Other values (65) 6191
28.0%

Hope_Optimism_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
No
620 
Yes
310 
69 

Length

Max length3
Median length2
Mean length2.1721722
Min length0

Characters and Unicode

Total characters2170
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 620
62.1%
Yes 310
31.0%
69
 
6.9%

Length

2025-07-03T06:21:21.475929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:21.543687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 620
66.7%
yes 310
33.3%

Most occurring characters

ValueCountFrequency (%)
N 620
28.6%
o 620
28.6%
Y 310
14.3%
e 310
14.3%
s 310
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 620
28.6%
o 620
28.6%
Y 310
14.3%
e 310
14.3%
s 310
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 620
28.6%
o 620
28.6%
Y 310
14.3%
e 310
14.3%
s 310
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 620
28.6%
o 620
28.6%
Y 310
14.3%
e 310
14.3%
s 310
14.3%
Distinct312
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size92.3 KiB
2025-07-03T06:21:21.837510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length206
Median length0
Mean length35.454454
Min length0

Characters and Unicode

Total characters35419
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique311 ?
Unique (%)31.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
a 430
 
7.8%
the 330
 
6.0%
and 279
 
5.1%
of 218
 
4.0%
for 206
 
3.7%
positive 175
 
3.2%
hope 160
 
2.9%
to 102
 
1.9%
offers 95
 
1.7%
sense 73
 
1.3%
Other values (1243) 3427
62.4%
2025-07-03T06:21:22.321347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5184
14.6%
e 3841
 
10.8%
o 2616
 
7.4%
i 2333
 
6.6%
a 2064
 
5.8%
s 2042
 
5.8%
n 1948
 
5.5%
t 1920
 
5.4%
r 1642
 
4.6%
l 1081
 
3.1%
Other values (65) 10748
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5184
14.6%
e 3841
 
10.8%
o 2616
 
7.4%
i 2333
 
6.6%
a 2064
 
5.8%
s 2042
 
5.8%
n 1948
 
5.5%
t 1920
 
5.4%
r 1642
 
4.6%
l 1081
 
3.1%
Other values (65) 10748
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5184
14.6%
e 3841
 
10.8%
o 2616
 
7.4%
i 2333
 
6.6%
a 2064
 
5.8%
s 2042
 
5.8%
n 1948
 
5.5%
t 1920
 
5.4%
r 1642
 
4.6%
l 1081
 
3.1%
Other values (65) 10748
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5184
14.6%
e 3841
 
10.8%
o 2616
 
7.4%
i 2333
 
6.6%
a 2064
 
5.8%
s 2042
 
5.8%
n 1948
 
5.5%
t 1920
 
5.4%
r 1642
 
4.6%
l 1081
 
3.1%
Other values (65) 10748
30.3%

Personal_Identification_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.3 KiB
Yes
734 
No
196 
 
69

Length

Max length3
Median length3
Mean length2.5965966
Min length0

Characters and Unicode

Total characters2594
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 734
73.5%
No 196
 
19.6%
69
 
6.9%

Length

2025-07-03T06:21:22.441692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-03T06:21:22.511007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 734
78.9%
no 196
 
21.1%

Most occurring characters

ValueCountFrequency (%)
Y 734
28.3%
e 734
28.3%
s 734
28.3%
N 196
 
7.6%
o 196
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 734
28.3%
e 734
28.3%
s 734
28.3%
N 196
 
7.6%
o 196
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 734
28.3%
e 734
28.3%
s 734
28.3%
N 196
 
7.6%
o 196
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 734
28.3%
e 734
28.3%
s 734
28.3%
N 196
 
7.6%
o 196
 
7.6%
Distinct736
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Memory size151.2 KiB
2025-07-03T06:21:22.820728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length252
Median length185
Mean length94.826827
Min length0

Characters and Unicode

Total characters94732
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique735 ?
Unique (%)73.6%

Sample

1st row
2nd rowDirectly targets 'Seniors' and 'Drivers Aged 70 and Above', creating immediate relevance for this demographic.
3rd rowReaders who are LGBTQ+, advocates for civil rights, or those with military connections may strongly identify with the cadets' situation and the implications of the decision.
4th rowThe terms 'drivers' and 'millions of drivers' directly target and appeal to a very large and identifiable group of readers.
5th rowAppeals directly to regular McDonald's breakfast consumers who might be affected by the change.
ValueCountFrequency (%)
the 1011
 
7.0%
to 584
 
4.0%
of 503
 
3.5%
and 496
 
3.4%
with 377
 
2.6%
or 370
 
2.6%
a 339
 
2.3%
directly 288
 
2.0%
identify 228
 
1.6%
in 227
 
1.6%
Other values (2152) 10016
69.4%
2025-07-03T06:21:23.315202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13704
14.5%
e 10282
 
10.9%
i 6536
 
6.9%
t 6368
 
6.7%
a 6089
 
6.4%
n 5916
 
6.2%
o 5558
 
5.9%
r 5127
 
5.4%
s 5051
 
5.3%
l 3739
 
3.9%
Other values (73) 26362
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13704
14.5%
e 10282
 
10.9%
i 6536
 
6.9%
t 6368
 
6.7%
a 6089
 
6.4%
n 5916
 
6.2%
o 5558
 
5.9%
r 5127
 
5.4%
s 5051
 
5.3%
l 3739
 
3.9%
Other values (73) 26362
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13704
14.5%
e 10282
 
10.9%
i 6536
 
6.9%
t 6368
 
6.7%
a 6089
 
6.4%
n 5916
 
6.2%
o 5558
 
5.9%
r 5127
 
5.4%
s 5051
 
5.3%
l 3739
 
3.9%
Other values (73) 26362
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13704
14.5%
e 10282
 
10.9%
i 6536
 
6.9%
t 6368
 
6.7%
a 6089
 
6.4%
n 5916
 
6.2%
o 5558
 
5.9%
r 5127
 
5.4%
s 5051
 
5.3%
l 3739
 
3.9%
Other values (73) 26362
27.8%

Interactions

2025-07-03T06:20:50.763320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-03T06:21:23.457175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Authority_PresentClarity_and_Conciseness_ValueContains_ColonContains_Exclamation_MarkContains_HyphenContains_NumbersContains_Question_MarkContains_QuotesCuriosity_PresentEconomic_Benefit_PresentEnds_With_Question_MarkExclusivity_PresentExclusivity_WordsFear_Concern_PresentHope_Optimism_PresentIndignation_Controversy_PresentLength_General_AssessmentMain_CategoryMain_ClassificationNational_Relevance_PresentOriginality_and_Differentiation_ValuePersonal_Identification_PresentProhibition_Restriction_PresentRecognized_Brand_PresentRelevance_and_Timeliness_ValueSolution_PresentStarts_With_NumberStrategic_Keyword_Usage_ValueSurprise_Awe_PresentTemporal_Urgency_PresentVisibility
Authority_Present1.0000.7060.7080.7070.7060.7070.7070.7060.7090.7060.7060.7070.7000.7120.7070.7080.7070.7260.7410.7070.7070.7080.7110.7070.7080.7060.7080.7080.7060.7061.000
Clarity_and_Conciseness_Value0.7061.0000.7060.7060.7060.7060.7060.7070.7060.7080.7060.7060.5710.7070.7060.7060.8010.5810.5740.7090.5930.7080.7080.7070.7070.7060.7060.7120.7070.7101.000
Contains_Colon0.7080.7061.0000.7070.7110.7060.7070.7250.7140.7070.7070.7100.7100.7070.7060.7060.7070.7140.7660.7070.7140.7070.7070.7060.7070.7060.7080.7070.7090.7081.000
Contains_Exclamation_Mark0.7070.7060.7071.0000.7060.7080.7060.7070.7070.7070.7060.7060.6880.7060.7070.7070.7060.7040.6900.7060.7060.7070.7060.7060.7060.7070.7070.7060.7060.7071.000
Contains_Hyphen0.7060.7060.7110.7061.0000.7130.7100.7070.7070.7060.7090.7080.7020.7070.7110.7080.7080.7200.7070.7070.7120.7080.7070.7070.7070.7080.7080.7070.7060.7081.000
Contains_Numbers0.7070.7060.7060.7080.7131.0000.7100.7060.7100.7200.7080.7080.7000.7080.7070.7070.7060.7110.7290.7060.7070.7070.7060.7060.7070.7070.7390.7060.7120.7091.000
Contains_Question_Mark0.7070.7060.7070.7060.7100.7101.0000.7080.7100.7070.9050.7070.6890.7060.7070.7070.7070.7050.9730.7060.7070.7080.7070.7100.7060.7070.7080.7070.7090.7071.000
Contains_Quotes0.7060.7070.7250.7070.7070.7060.7081.0000.7110.7060.7070.7080.7090.7070.7100.7110.7080.7280.8020.7120.7250.7070.7070.7090.7120.7130.7080.7080.7180.7071.000
Curiosity_Present0.7090.7060.7140.7070.7070.7100.7100.7111.0000.7080.7090.7080.6950.7210.7080.7060.7060.7430.7340.7230.7320.7060.7070.7060.7070.7080.7090.7080.7310.7081.000
Economic_Benefit_Present0.7060.7080.7070.7070.7060.7200.7070.7060.7081.0000.7070.7070.7060.7090.7200.7090.7110.7430.6980.7060.7080.7070.7070.7120.7060.7060.7080.7070.7100.7071.000
Ends_With_Question_Mark0.7060.7060.7070.7060.7090.7080.9050.7070.7090.7071.0000.7070.6890.7060.7060.7070.7060.7070.8850.7070.7070.7070.7070.7080.7060.7060.7070.7070.7080.7061.000
Exclusivity_Present0.7070.7060.7100.7060.7080.7080.7070.7080.7080.7070.7071.0000.9870.7090.7070.7060.7080.7080.7060.7060.7110.7100.7060.7060.7070.7070.7080.7070.7150.7061.000
Exclusivity_Words0.7000.5710.7100.6880.7020.7000.6890.7090.6950.7060.6890.9871.0000.6960.6990.6980.7170.2830.2500.6980.5800.7090.7020.6980.7080.6950.6900.6890.7060.7011.000
Fear_Concern_Present0.7120.7070.7070.7060.7070.7080.7060.7070.7210.7090.7060.7090.6961.0000.7400.7390.7080.7920.7160.7150.7080.7100.7260.7060.7100.7120.7100.7060.7090.7081.000
Hope_Optimism_Present0.7070.7060.7060.7070.7110.7070.7070.7100.7080.7200.7060.7070.6990.7401.0000.7290.7090.7650.7130.7150.7080.7190.7080.7090.7110.7970.7140.7070.7130.7071.000
Indignation_Controversy_Present0.7080.7060.7060.7070.7080.7070.7070.7110.7060.7090.7070.7060.6980.7390.7291.0000.7110.7480.7190.7120.7180.7090.7110.7080.7060.7170.7080.7070.7170.7061.000
Length_General_Assessment0.7070.8010.7070.7060.7080.7060.7070.7080.7060.7110.7060.7080.7170.7080.7090.7111.0000.7120.7170.7090.7090.7070.7070.7080.7070.7070.7090.7080.7080.7081.000
Main_Category0.7260.5810.7140.7040.7200.7110.7050.7280.7430.7430.7070.7080.2830.7920.7650.7480.7121.0000.3070.7870.6020.7560.7430.7530.7080.7780.7230.7080.7480.7121.000
Main_Classification0.7410.5740.7660.6900.7070.7290.9730.8020.7340.6980.8850.7060.2500.7160.7130.7190.7170.3071.0000.7120.6000.7050.7020.7110.7020.7170.8440.6970.7330.7141.000
National_Relevance_Present0.7070.7090.7070.7060.7070.7060.7060.7120.7230.7060.7070.7060.6980.7150.7150.7120.7090.7870.7121.0000.7100.7070.7070.7110.7070.7190.7060.7070.7130.7081.000
Originality_and_Differentiation_Value0.7070.5930.7140.7060.7120.7070.7070.7250.7320.7080.7070.7110.5800.7080.7080.7180.7090.6020.6000.7101.0000.7240.7060.7090.7060.7090.7070.7110.8080.7061.000
Personal_Identification_Present0.7080.7080.7070.7070.7080.7070.7080.7070.7060.7070.7070.7100.7090.7100.7190.7090.7070.7560.7050.7070.7241.0000.7100.7070.7080.7140.7080.7070.7350.7071.000
Prohibition_Restriction_Present0.7110.7080.7070.7060.7070.7060.7070.7070.7070.7070.7070.7060.7020.7260.7080.7110.7070.7430.7020.7070.7060.7101.0000.7060.7070.7060.7070.7060.7070.7101.000
Recognized_Brand_Present0.7070.7070.7060.7060.7070.7060.7100.7090.7060.7120.7080.7060.6980.7060.7090.7080.7080.7530.7110.7110.7090.7070.7061.0000.7060.7170.7150.7060.7090.7071.000
Relevance_and_Timeliness_Value0.7080.7070.7070.7060.7070.7070.7060.7120.7070.7060.7060.7070.7080.7100.7110.7060.7070.7080.7020.7070.7060.7080.7070.7061.0000.7080.7070.7350.7070.7071.000
Solution_Present0.7060.7060.7060.7070.7080.7070.7070.7130.7080.7060.7060.7070.6950.7120.7970.7170.7070.7780.7170.7190.7090.7140.7060.7170.7081.0000.7080.7080.7150.7071.000
Starts_With_Number0.7080.7060.7080.7070.7080.7390.7080.7080.7090.7080.7070.7080.6900.7100.7140.7080.7090.7230.8440.7060.7070.7080.7070.7150.7070.7081.0000.7070.7090.7081.000
Strategic_Keyword_Usage_Value0.7080.7120.7070.7060.7070.7060.7070.7080.7080.7070.7070.7070.6890.7060.7070.7070.7080.7080.6970.7070.7110.7070.7060.7060.7350.7080.7071.0000.7070.7061.000
Surprise_Awe_Present0.7060.7070.7090.7060.7060.7120.7090.7180.7310.7100.7080.7150.7060.7090.7130.7170.7080.7480.7330.7130.8080.7350.7070.7090.7070.7150.7090.7071.0000.7081.000
Temporal_Urgency_Present0.7060.7100.7080.7070.7080.7090.7070.7070.7080.7070.7060.7060.7010.7080.7070.7060.7080.7120.7140.7080.7060.7070.7100.7070.7070.7070.7080.7060.7081.0001.000
Visibility1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2025-07-03T06:20:58.106035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-03T06:20:58.547123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TitleVisibilityCleaned_HeadlineMain_CategorySubcategory_1Subcategory_2Clarity_and_Conciseness_ValueClarity_and_Conciseness_CommentRelevance_and_Timeliness_ValueRelevance_and_Timeliness_CommentStrategic_Keyword_Usage_ValueStrategic_Keyword_Usage_CommentOriginality_and_Differentiation_ValueOriginality_and_Differentiation_CommentContains_NumbersContains_QuotesContains_Question_MarkContains_ColonContains_HyphenContains_Exclamation_MarkStarts_With_NumberEnds_With_Question_MarkLength_General_AssessmentLength_Number_of_CharactersEmphatic_Capitalization_UsageMain_ClassificationComment_Type_JustificationTemporal_Urgency_PresentTemporal_Urgency_WordsExclusivity_PresentExclusivity_WordsAuthority_PresentAuthority_WordsSolution_PresentSolution_WordsEconomic_Benefit_PresentEconomic_Benefit_WordsProhibition_Restriction_PresentProhibition_Restriction_WordsNational_Relevance_PresentNational_Relevance_WordsRecognized_Brand_PresentRecognized_Brand_WordsCuriosity_PresentCuriosity_EvidenceFear_Concern_PresentFear_Concern_EvidenceSurprise_Awe_PresentSurprise_Awe_EvidenceIndignation_Controversy_PresentIndignation_Controversy_EvidenceHope_Optimism_PresentHope_Optimism_EvidencePersonal_Identification_PresentPersonal_Identification_Evidence
0Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand22572066Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brandFinance_and_BusinessCompanies & EntrepreneurshipN/AHighThe main message is very clear and easy to understand, detailing Ford's production halt and the CEO's admission of struggle.HighThe headline discusses a significant event for a major global brand, which is highly relevant and timely for business and general news audiences.HighKey terms like 'Ford', 'factories', 'car production', 'CEO', and 'struggle' are used effectively, making the headline highly discoverable and appealing.MediumWhile the subject matter is impactful, the phrasing is a standard news report style, not particularly unique in its linguistic approach.NoYesNoNoNoNoNoNoAdequate105NoDeclarative SimpleThe headline presents a series of factual statements about Ford's situation and the CEO's admission without posing a question, using urgency markers, or comparing elements.Yes["immediately"]No[]Yes["CEO admits"]No[]No[]No[]No[]Yes["Ford"]NoYesThe phrases 'forced to immediately shut down factories', 'halt car production', and 'struggle for brand' evoke concern about economic stability and the future of a major company.NoNoNoNo
1New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above21331409New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and AboveNews_and_Current_EventsPoliticsGovernmentHighThe main message is exceptionally clear, detailing who, what, and when without ambiguity.HighHighly relevant to a specific, large demographic (seniors/drivers) and provides timely information about a future change.HighContains highly relevant keywords like 'U.S. Driving License Rule', 'Seniors', 'July 2025', 'Essential Changes', and 'Drivers Aged 70 and Above'.MediumWhile a standard news announcement format, the specific details make it distinct, yet it lacks a unique angle or creative phrasing.YesNoNoNoYesNoNoNoAdequate101NoDeclarative SimpleThe headline directly states a new rule and its implications, serving as a straightforward announcement.Yes["Begins July 2025"]No[]No[]No[]No[]No[]Yes["U.S."]No[]YesThe phrase 'Essential Changes' prompts readers to seek information on what those changes entail.NoNoNoNoYesDirectly targets 'Seniors' and 'Drivers Aged 70 and Above', creating immediate relevance for this demographic.
2Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender19344936Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgenderNews_and_Current_EventsPoliticsGovernmentHighThe main message is very clear and direct, stating precisely what occurred and why.HighThe topic of transgender rights and military policy is highly current and relevant, resonating with ongoing social and political discussions.HighUses strong, specific keywords like 'Cadets', 'Air Force Academy', 'denied commissions', and 'transgender', which are highly relevant to the subject matter and discoverable.MediumThe specific event described is notable, though the broader subject of transgender individuals in the military has been discussed before. It offers a distinct event within a known debate.NoNoNoNoNoNoNoNoAdequate106NoDeclarative SimpleThe headline makes a direct statement about an event, presenting it as a fact without using a question, direct quote, or emphasizing urgency.No[]No[]No[]No[]No[]Yes["denied"]No[]No[]NoNoNoYesThe phrase 'denied commissions because they’re transgender' directly implies a potential act of discrimination, which is highly likely to provoke indignation and controversy among various audiences.NoYesReaders who are LGBTQ+, advocates for civil rights, or those with military connections may strongly identify with the cadets' situation and the implications of the decision.
3The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently18797641The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgentlyNews_and_Current_EventsPoliticsGovernmentHighThe main message is very clear and direct, stating the change, its source, and its impact.HighHighly relevant as it affects a large demographic ('millions of drivers') and requires urgent action, indicating timeliness.HighUses strong keywords like 'DMV', 'United States', 'expired license', 'drivers', and 'renew urgently', which are highly searchable and relevant.MediumWhile the specific policy change is unique, the headline structure (authority confirms X - consequence) is common. Its strength lies in the immediate, widespread impact.YesNoNoNoYesNoNoNoAdequate173NoUrgencyThe headline clearly states a new policy confirmed by an authority and strongly emphasizes the immediate, widespread need for action, particularly with the phrase 'renew urgently' affecting 'millions of drivers'.Yes["immediate","urgently"]No[]Yes["DMV","confirms"]No[]No[]Yes["ending the benefit","without immediate renewal"]Yes["United States"]Yes["DMV"]NoYesThe phrase 'millions of drivers will have to renew urgently' directly implies potential negative consequences or significant inconvenience if the reader fails to act, evoking concern.NoNoNoYesThe terms 'drivers' and 'millions of drivers' directly target and appeal to a very large and identifiable group of readers.
4McDonald's Removing 1 Breakfast Menu Item for Good on July 216353543McDonald's Removing 1 Breakfast Menu Item for Good on July 2GastronomyRestaurants & ChefsN/AHighThe message is clear and to the point, identifying the brand, action, and date.HighHighly relevant for McDonald's customers and tied to a specific future date, July 2.HighUses strong keywords like 'McDonald's', 'Breakfast Menu', and 'Removing', appealing to target audience interests.MediumWhile common for fast-food news, the specificity of '1 Breakfast Menu Item' and 'for Good' provides some differentiation.YesNoNoNoNoNoNoNoAdequate57NoDeclarative SimpleThe headline directly states a fact without posing a question, using a quote, or indicating urgency beyond the date.No[]No[]No[]No[]No[]No[]No[]Yes["McDonald's"]YesThe headline creates an information gap by not revealing which specific breakfast item is being removed.YesThe phrase 'Removing... for Good' could evoke concern among customers about a favorite item.NoNoNoYesAppeals directly to regular McDonald's breakfast consumers who might be affected by the change.
5The DMV confirms it—people over 70 will have to meet new requirements to keep their driver's license in the United States15318643The DMV confirms it—people over 70 will have to meet new requirements to keep their driver's license in the United StatesNews_and_Current_EventsPoliticsGovernmentHighThe main message about new DMV requirements for older drivers is very clear and easy to understand.HighDriving regulations, especially those affecting a specific age group, are highly relevant to a large demographic and are a recurring topic of public interest.HighKeywords like 'DMV', '70', 'driver's license', and 'United States' are highly relevant and likely to be searched for or noticed by the target audience.MediumThe headline is specific about the authority ('DMV confirms it') and the demographic, which gives it some differentiation from generic news, but the structure is standard.YesNoNoNoYesNoNoNoAdequate106NoDeclarative SimpleThe headline directly states a fact confirmed by an authority, informing the reader of a new regulation without posing a question or implying a mystery.No[]No[]Yes["confirms","DMV"]No[]No[]Yes["will have to meet new requirements","keep their driver's license"]Yes["United States"]Yes["DMV"]YesThe mention of 'new requirements' without specifying them creates an information gap, prompting readers to click to learn what these changes entail.YesThe phrase 'will have to meet new requirements to keep their driver's license' can evoke concern or anxiety for older drivers about their ability to retain their driving privileges and independence.NoNoNoYesThe headline directly addresses and impacts 'people over 70', creating strong personal identification for individuals in that age group and their families.
6Goodbye to retirement at 65: Social Security sets a new retirement age from 202614627061Goodbye to retirement at 65: Social Security sets a new retirement age from 2026Finance_and_BusinessPersonal_FinanceRetirementHighThe main message is immediately clear: the retirement age is changing.HighThe topic of retirement age and Social Security is highly relevant to a broad audience and includes a specific future date ('from 2026') indicating timeliness.HighKey terms like 'retirement,' 'Social Security,' and 'retirement age' are highly strategic and likely to be searched by interested individuals.MediumWhile retirement changes are common news, the opening phrase 'Goodbye to retirement at 65' adds an impactful and somewhat unique framing.YesNoNoYesNoNoNoNoAdequate70NoDeclarative SimpleThe headline presents a direct statement of fact about a change in the retirement age.Yes["from 2026"]No[]Yes["Social Security"]No[]No[]No[]No[]Yes["Social Security"]YesThe phrase 'Goodbye to retirement at 65' immediately piques curiosity about what the new age will be and why the change is happening.YesThe headline could evoke concern among readers about their financial future and the implications of a later retirement.YesThe news of a fundamental shift in retirement age, a long-established concept, can be surprising.YesChanges to social programs like retirement age frequently generate public debate and potential indignation.NoYesThe topic of retirement and Social Security directly affects the financial planning and future of many individuals, leading to strong personal identification.
7Elon Musk gave Apple 72 hours to accept his $5 billion offer. Tim Cook said no, so Elon followed through with his threat.13507301Elon Musk gave Apple 72 hours to accept his $5 billion offer. Tim Cook said no, so Elon followed through with his threat.Finance_and_BusinessCompanies & EntrepreneurshipN/AHighThe main message is very clear, detailing the involved parties, the offer, the rejection, and the subsequent action, without ambiguity.HighFeatures highly relevant and frequently discussed figures (Elon Musk, Tim Cook) and companies (Apple), ensuring strong general interest.HighIncludes prominent keywords such as 'Elon Musk', 'Apple', 'Tim Cook', and '$5 billion', which are highly searchable and appealing to a broad audience.HighThe narrative of a public challenge, rejection, and explicit follow-through on a 'threat' provides a unique and dramatic angle that stands out.YesNoNoNoNoNoNoNoAdequate104NoDeclarative SimpleThe headline presents a straightforward statement of facts without posing a question, quoting directly, or explicitly indicating urgency.Yes["72 hours"]No[]No[]No[]Yes["$5 billion"]No[]No[]Yes["Elon Musk","Apple","Tim Cook"]YesThe phrase 'followed through with his threat' creates an information gap, compelling the reader to wonder what the threat was and its consequences.NoYesThe audacious $5 billion offer with a short ultimatum and the dramatic follow-through on a 'threat' can evoke surprise.YesThe nature of the 'threat' and its execution can spark debate or strong opinions about the involved parties' actions.NoNo
890s Country Icon’s Teeth Fall Out Mid-Performance During Washington Concert: “The Show Must Go On”1333995290s Country Icon’s Teeth Fall Out Mid-Performance During Washington Concert: “The Show Must Go On”Entertainment_and_CultureCelebrities_and_InfluencersN/AHighThe main message is straightforward and easy to understand, describing a highly unusual and specific event.HighThe headline describes an unusual and attention-grabbing incident involving a celebrity, which is highly relevant to current interests in entertainment news.HighKeywords like "90s Country Icon," "Teeth Fall Out," and "Mid-Performance" are highly specific and engaging, likely to capture audience attention.HighThe event described is extremely unusual and therefore the headline is highly original and stands out from typical news.YesYesNoYesNoNoYesNoAdequate101Yes, "The Show Must Go On", justified use as it is a direct quote.Direct QuoteThe headline prominently features a direct quote from the event, which is essential to its content and impact.No[]No[]No[]No[]No[]No[]Yes["Washington"]No[]YesThe phrase "Teeth Fall Out Mid-Performance" creates a strong information gap and immediately makes the reader curious about the circumstances.NoYesThe highly unexpected and bizarre nature of "Teeth Fall Out Mid-Performance" elicits surprise and possibly a sense of awe at the unusual incident.NoNoNo
9Americans who own refrigerators from 3 brands to get $300 from settlement13255807Americans who own refrigerators from 3 brands to get $300 from settlementFinance_and_BusinessPersonal_FinanceN/AHighThe main message is very clear: certain Americans will receive money due to a settlement.HighDirectly impacts a large segment of the population (Americans who own specific appliances) and offers a tangible benefit (money from a settlement).HighIncludes highly relevant keywords like 'Americans', 'refrigerators', 'brands', '$300', and 'settlement', which are likely search terms for interested individuals.MediumWhile the specific settlement details are unique, the headline structure of 'who gets money from X' is a common news format. The unnamed brands reduce its immediate differentiation.YesNoNoNoNoNoNoNoAdequate70NoDeclarative SimpleThe headline presents a direct and straightforward statement of fact without asking a question, making a comparison, or implying urgency.No[]No[]No[]No[]Yes["$300","settlement"]No[]Yes["Americans"]No[]YesThe mention of '3 brands' creates a curiosity gap, prompting readers to click to find out which brands are involved.NoNoNoYesThe prospect of receiving $300 from a settlement evokes a sense of hope and financial optimism for those who qualify.YesAmericans who own refrigerators' directly targets a broad demographic, encouraging self-identification and relevance.
TitleVisibilityCleaned_HeadlineMain_CategorySubcategory_1Subcategory_2Clarity_and_Conciseness_ValueClarity_and_Conciseness_CommentRelevance_and_Timeliness_ValueRelevance_and_Timeliness_CommentStrategic_Keyword_Usage_ValueStrategic_Keyword_Usage_CommentOriginality_and_Differentiation_ValueOriginality_and_Differentiation_CommentContains_NumbersContains_QuotesContains_Question_MarkContains_ColonContains_HyphenContains_Exclamation_MarkStarts_With_NumberEnds_With_Question_MarkLength_General_AssessmentLength_Number_of_CharactersEmphatic_Capitalization_UsageMain_ClassificationComment_Type_JustificationTemporal_Urgency_PresentTemporal_Urgency_WordsExclusivity_PresentExclusivity_WordsAuthority_PresentAuthority_WordsSolution_PresentSolution_WordsEconomic_Benefit_PresentEconomic_Benefit_WordsProhibition_Restriction_PresentProhibition_Restriction_WordsNational_Relevance_PresentNational_Relevance_WordsRecognized_Brand_PresentRecognized_Brand_WordsCuriosity_PresentCuriosity_EvidenceFear_Concern_PresentFear_Concern_EvidenceSurprise_Awe_PresentSurprise_Awe_EvidenceIndignation_Controversy_PresentIndignation_Controversy_EvidenceHope_Optimism_PresentHope_Optimism_EvidencePersonal_Identification_PresentPersonal_Identification_Evidence
989New tax hikes on a variety of items coming to Illinois next month. Here's what prices are going up and when590578New tax hikes on a variety of items coming to Illinois next month. Here's what prices are going up and whenFinance_and_BusinessPersonal FinanceN/AHighThe headline clearly states the subject (tax hikes), location (Illinois), and implication (prices going up), and when (next month).HighThe topic of tax hikes and rising prices is highly relevant to personal finance and timely due to the 'next month' mention.HighUses strong keywords like 'tax hikes', 'Illinois', and 'prices going up' which are highly relevant and searchable.MediumWhile tax hike news is common, the second sentence directly addressing 'what prices' and 'when' adds a valuable and somewhat unique angle, though the core topic is not novel.NoNoNoYesNoNoNoNoAdequate101NoMystery/RevelationThe headline, particularly the second part 'Here's what prices are going up and when', promises to reveal specific information, creating an information gap that readers will want to fill.Yes["next month"]No[]No[]No[]No[]No[]Yes["Illinois"]No[]YesHere's what prices are going up and whenYesNew tax hikes, prices are going upNoYesNew tax hikesNoYesIllinois, prices are going up
990Illinois residents' information accessed in data breach, Healthcare and Family Services says589230Illinois residents' information accessed in data breach, Healthcare and Family Services saysNews_and_Current_EventsCrime & JudicialN/AHighThe main message is clear and easy to understand, directly stating the event and its source.HighData breaches are a highly relevant and timely topic, especially for the affected demographic.HighThe headline effectively uses relevant keywords such as "Illinois residents", "information accessed", and "data breach".MediumWhile data breaches are common, the specific mention of "Illinois residents" and the government agency provides some differentiation.NoNoNoNoNoNoNoNoAdequate84NoAttribution ('according to', 'reveals')The headline explicitly states the source of the information, "Healthcare and Family Services says", making it an attribution type.No[]No[]Yes["says"]No[]No[]No[]Yes["Illinois"]Yes["Healthcare and Family Services"]NoYesThe phrase "data breach" and "information accessed" immediately triggers concern regarding personal security and privacy.NoNoNoYesThe mention of "Illinois residents" directly targets and makes the headline relevant to a specific audience.
991A Buc-ee’s competitor is marking its territory across Texas588887A Buc-ee’s competitor is marking its territory across TexasFinance_and_BusinessCompanies & EntrepreneurshipCompetitionHighThe headline is very clear and easy to understand.HighHighly relevant to Texans and those interested in travel/business news due to the popularity of Buc-ee's.HighUses strong keywords like "Buc-ee's", "competitor", and "Texas" which are highly appealing to the target audience.HighThe phrase "marking its territory" adds a unique and evocative angle, making it stand out.NoNoNoNoYesNoNoNoAdequate55NoDeclarative SimpleThe headline makes a straightforward statement of fact without asking a question, quoting someone, or indicating urgency.No[]No[]No[]No[]No[]No[]Yes["Texas"]Yes["Buc-ee’s"]YesThe mention of a "Buc-ee’s competitor" and "marking its territory" creates an information gap, making the reader curious about who the competitor is and what they are doing.NoNoNoNoYesThe headline directly appeals to individuals who are familiar with or reside in Texas and are aware of Buc-ee's.
992Sam's Club sends a last-minute message to its members: It's here already587557Sam's Club sends a last-minute message to its members: It's here alreadyFinance_and_BusinessCompanies & EntrepreneurshipN/AHighThe main message is clear about the sender and recipient, and implies an important arrival.HighThe 'last-minute' and 'already' phrases create immediate relevance and urgency for Sam's Club members.MediumUses "Sam's Club" and "members" which are relevant, but the ambiguous "It's here already" might not optimize for specific searches.MediumThe specific phrasing with the intriguing "It's here already" provides a unique angle compared to a purely informative headline.NoYesNoYesYesNoNoNoAdequate70NoMystery/RevelationThe headline hints at the arrival of something significant ("It's here already") but doesn't reveal what it is, thereby building anticipation and curiosity.Yes["last-minute","already"]No[]No[]No[]No[]No[]No[]Yes["Sam's Club"]YesThe phrase 'It's here already' creates an information gap, making the reader wonder what 'it' is.NoNoNoNoYesThe phrase 'to its members' directly targets a specific audience, making the message feel personal and relevant to them.
993Jennifer Aniston's home features the most glamorous garden lounge we've ever seen – it is five-star perfection586529Jennifer Aniston's home features the most glamorous garden lounge we've ever seen – it is five-star perfectionHome_and_LifestyleDecoration_and_Interior_DesignN/AHighThe message is very clear and easy to understand, directly stating the subject and its perceived quality.HighCelebrity homes and interior design are consistently high-interest evergreen topics.HighUses strong keywords like 'Jennifer Aniston', 'home', 'garden lounge', and descriptive terms like 'glamorous' and 'five-star perfection' appealing to the target audience.MediumWhile celebrity home features are common, the strong superlative claim of "most glamorous...we've ever seen" provides a level of differentiation.YesNoNoNoNoNoNoNoAdequate80NoSuperlativeThe headline explicitly uses superlative language such as "most glamorous" and "five-star perfection" to describe the subject.No[]No[]No[]No[]No[]No[]No[]Yes["Jennifer Aniston"]YesThe phrase "the most glamorous garden lounge we've ever seen – it is five-star perfection" creates an information gap, making readers curious to see what makes it so exceptional.NoYesThe strong superlative "most glamorous" and "five-star perfection" is intended to evoke a sense of awe and wonder at the described feature.NoNoYesReaders can easily imagine and aspire to have such a glamorous feature in their own homes, leading to personal identification with the desirable element.
99418 High-Protein No-Cook Recipes for Dinner Tonight58542818 High-Protein No-Cook Recipes for Dinner TonightGastronomyRecipesMain CoursesHighThe main message is very clear: 18 high-protein, no-cook recipes for dinner tonight. No ambiguity.HighHighly relevant to current interests in healthy eating and convenience, with a clear timely hook ("Tonight").HighUses strong keywords like "High-Protein", "No-Cook", "Recipes", and "Dinner Tonight" that align with user searches and interests.MediumWhile recipe headlines are common, the specific combination of "High-Protein" and "No-Cook" offers a somewhat unique and appealing angle.YesNoNoNoYesNoYesNoAdequate53NoList/NumberedThe headline explicitly starts with a number (18), indicating a list of items.Yes["Tonight"]No[]No[]Yes["Recipes","No-Cook"]No[]Yes["No-Cook"]No[]No[]YesThe specific number "18" and the unique combination of "High-Protein" and "No-Cook" create an information gap that sparks curiosity about the recipes themselves.NoNoNoYesOffers a convenient, healthy, and easy solution for dinner, evoking a sense of relief and positivity.YesDirectly addresses a common need ("for Dinner Tonight") and targets individuals seeking healthy ("High-Protein") and easy ("No-Cook") meal solutions, making it highly relatable.
995Map Shows the 50 Best School Districts Across US585417Map Shows the 50 Best School Districts Across USEducation_and_GuidesAcademic AdviceN/AHighThe main message is very clear and easy to understand.HighEducation quality and school rankings are consistently relevant to a broad audience.HighUses highly relevant keywords like "Map", "50 Best", "School Districts", and "US".MediumWhile listicles are common, the "Map Shows" aspect adds a slight visual and informative differentiator.YesNoNoNoNoNoNoNoAdequate46NoList/Numbered ('50 Best')The headline explicitly mentions "the 50 Best", classifying it as a numbered list and superlative.No[]No[]No[]No[]No[]No[]Yes["US"]No[]YesThe headline creates an information gap by promising to reveal the "50 Best School Districts" that a map shows.NoNoNoYesThe phrase "Best School Districts" appeals to the hope of finding quality education and a positive future.YesThe topic of school districts in the "US" directly appeals to parents, students, and educators residing in the country.
996GPS-based speed limiter will be mandatory ― One state approves it and announces the date585267
997McDonald's Is Bringing Back a Sandwich We Never Thought We'd See Again583765McDonald's Is Bringing Back a Sandwich We Never Thought We'd See AgainGastronomyRestaurants & ChefsN/AHighThe main message is immediately clear and easy to understand.HighFood news, especially about popular fast-food chains and nostalgic items, generally has high relevance and evergreen interest.HighUses strong, relevant keywords like 'McDonald's' and 'Sandwich', which are highly searchable and appealing to the target audience.MediumWhile the theme of a returning menu item is common, the phrase "We Never Thought We'd See Again" adds a unique angle and creates intrigue.NoNoNoNoNoNoNoNoAdequate66NoMystery/Revelation ('secret', 'truth')The headline creates a strong sense of mystery and anticipation by implying the return of a popular item that was believed to be gone forever, compelling the reader to discover what it is.No[]No[]No[]No[]No[]No[]No[]Yes["McDonald's"]YesThe phrase "We Never Thought We'd See Again" creates an information gap, making readers curious about which sandwich is returning.NoYesThe unexpected return implied by "We Never Thought We'd See Again" evokes surprise and a sense of delight for fans of the item.NoYesFor fans of the particular sandwich, its return brings a sense of hope and optimism about being able to enjoy it again.YesThe use of "We" directly includes the reader, fostering a sense of shared nostalgia and anticipation for the returning item.
998This City Was Just Named the No. 1 Place to Live in the U.S. for Affordability and Cost of Living582886